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GitHub - jac99/MinkLocMultimodal: MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition
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1-.355-.508h-.016.016Zm.641-2.935c.136 1.057.403 1.913.878 2.497.442.544 1.134.938 2.344.938 1.573 0 2.292-.337 2.657-.751.384-.435.558-1.15.558-2.361 0-1.14-.243-1.847-.705-2.319-.477-.488-1.319-.862-2.824-1.025-1.487-.161-2.192.138-2.533.529-.269.307-.437.808-.438 1.578v.021c0 .265.021.562.063.893Zm-1.626 0c.042-.331.063-.628.063-.894v-.02c-.001-.77-.169-1.271-.438-1.578-.341-.391-1.046-.69-2.533-.529-1.505.163-2.347.537-2.824 1.025-.462.472-.705 1.179-.705 2.319 0 1.211.175 1.926.558 2.361.365.414 1.084.751 2.657.751 1.21 0 1.902-.394 2.344-.938.475-.584.742-1.44.878-2.497Z"></path><path d="M14.5 14.25a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Copilot</div> Write better code with AI </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" 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2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> </ul> </div> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Solutions <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon 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0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 px-lg-4"> <div class="HeaderMenu-column"> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{"location":"navbar","action":"github_sponsors","context":"open_source","tag":"link","label":"github_sponsors_link_open_source_navbar"}" href="/sponsors"> <div> <div class="color-fg-default h4">GitHub Sponsors</div> Fund open source developers </div> </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center 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dir=\"auto\"\u003eMinkLoc++: Lidar and Monocular Image Fusion for Place Recognition\u003c/h1\u003e\u003ca id=\"user-content-minkloc-lidar-and-monocular-image-fusion-for-place-recognition\" class=\"anchor\" aria-label=\"Permalink: MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition\" href=\"#minkloc-lidar-and-monocular-image-fusion-for-place-recognition\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePaper: \u003ca href=\"https://ieeexplore.ieee.org/document/9533373\" rel=\"nofollow\"\u003eMinkLoc++: Lidar and Monocular Image Fusion for Place Recognition\u003c/a\u003e\naccepted for International Joint Conference on Neural Networks (IJCNN) 2021\n\u003ca href=\"https://arxiv.org/pdf/2104.05327.pdf\" rel=\"nofollow\"\u003eArXiv\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"mailto:jacek.komorowski@pw.edu.pl\"\u003eJacek Komorowski\u003c/a\u003e, Monika Wysoczańska, Tomasz Trzciński\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWarsaw University of Technology\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eOur other projects\u003c/h3\u003e\u003ca id=\"user-content-our-other-projects\" class=\"anchor\" aria-label=\"Permalink: Our other projects\" href=\"#our-other-projects\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMinkLoc3D: Point Cloud Based Large-Scale Place Recognition (WACV 2021): \u003ca href=\"https://github.com/jac99/MinkLoc3D\"\u003eMinkLoc3D\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eLarge-Scale Topological Radar Localization Using Learned Descriptors (ICONIP 2021): \u003ca href=\"https://github.com/jac99/RadarLoc\"\u003eRadarLoc\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eEgonNN: Egocentric Neural Network for Point Cloud Based 6DoF Relocalization at the City Scale (IEEE Robotics and Automation Letters April 2022): \u003ca href=\"https://github.com/jac99/Egonn\"\u003eEgoNN\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eImproving Point Cloud Based Place Recognition with Ranking-based Loss and Large Batch Training (2022): \u003ca href=\"https://github.com/jac99/MinkLoc3Dv2\"\u003eMinkLoc3Dv2\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eIntroduction\u003c/h3\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe present a discriminative multimodal descriptor based on a pair of sensor readings: a point cloud from a LiDAR and an image from an RGB camera.\nOur descriptor, named MinkLoc++, can be used for place recognition, re-localization and loop closure purposes in robotics or autonomous vehicles applications.\nWe use late fusion approach, where each modality is processed separately and fused in the final part of the processing pipeline.\nThe proposed method achieves state-of-the-art performance on standard place recognition benchmarks.\nWe also identify dominating modality problem when training a multimodal descriptor.\nThe problem manifests itself when the network focuses on a modality with a larger overfit to the training data. This drives the loss down during the training but leads to suboptimal performance on the evaluation set.\nIn this work we describe how to detect and mitigate such risk when using a deep metric learning approach to train a multimodal neural network.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/jac99/MinkLocMultimodal/blob/master/media/overview.jpg\"\u003e\u003cimg src=\"/jac99/MinkLocMultimodal/raw/master/media/overview.jpg\" alt=\"Overview\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCitation\u003c/h3\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you find this work useful, please consider citing:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@INPROCEEDINGS{9533373, \n author={Komorowski, Jacek and Wysoczańska, Monika and Trzcinski, Tomasz}, \n booktitle={2021 International Joint Conference on Neural Networks (IJCNN)}, \n title={MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition}, \n year={2021}, \n doi={10.1109/IJCNN52387.2021.9533373}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@INPROCEEDINGS{9533373, \n author={Komorowski, Jacek and Wysoczańska, Monika and Trzcinski, Tomasz}, \n booktitle={2021 International Joint Conference on Neural Networks (IJCNN)}, \n title={MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition}, \n year={2021}, \n doi={10.1109/IJCNN52387.2021.9533373}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eEnvironment and Dependencies\u003c/h3\u003e\u003ca id=\"user-content-environment-and-dependencies\" class=\"anchor\" aria-label=\"Permalink: Environment and Dependencies\" href=\"#environment-and-dependencies\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eCode was tested using Python 3.8 with PyTorch 1.9.1 and MinkowskiEngine 0.5.4 on Ubuntu 20.04 with CUDA 10.2.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe following Python packages are required:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePyTorch (version 1.9.1)\u003c/li\u003e\n\u003cli\u003eMinkowskiEngine (version 0.5.4)\u003c/li\u003e\n\u003cli\u003epytorch_metric_learning (version 1.0 or above)\u003c/li\u003e\n\u003cli\u003etensorboard\u003c/li\u003e\n\u003cli\u003ecolour_demosaicing\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eModify the \u003ccode\u003ePYTHONPATH\u003c/code\u003e environment variable to include absolute path to the project root folder:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"export PYTHONPATH=$PYTHONPATH:/home/.../MinkLocMultimodal\"\u003e\u003cpre lang=\"export\" class=\"notranslate\"\u003e\u003ccode\u003eexport PYTHONPATH=$PYTHONPATH:/home/.../MinkLocMultimodal\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDatasets\u003c/h3\u003e\u003ca id=\"user-content-datasets\" class=\"anchor\" aria-label=\"Permalink: Datasets\" href=\"#datasets\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMinkLoc++\u003c/strong\u003e is a multimodal descriptor based on a pair of inputs:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ea 3D point cloud constructed by aggregating multiple 2D LiDAR scans from Oxford RobotCar dataset,\u003c/li\u003e\n\u003cli\u003ea corresponding RGB image from the stereo-center camera.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eWe use 3D point clouds built by authors of \u003cem\u003ePointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition\u003c/em\u003e paper (\u003ca href=\"https://arxiv.org/pdf/1804.03492\" rel=\"nofollow\"\u003elink\u003c/a\u003e).\nEach point cloud is built by aggregating 2D LiDAR scans gathered during the 20 meter vehicle traversal.\nFor details see PointNetVLAD paper or their github repository (\u003ca href=\"https://github.com/mikacuy/pointnetvlad\"\u003elink\u003c/a\u003e).\nYou can download training and evaluation point clouds from\n\u003ca href=\"https://drive.google.com/open?id=1rflmyfZ1v9cGGH0RL4qXRrKhg-8A-U9q\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\n(\u003ca href=\"https://drive.google.com/file/d/1-1HA9Etw2PpZ8zHd3cjrfiZa8xzbp41J/view?usp=sharing\" rel=\"nofollow\"\u003ealternative link\u003c/a\u003e).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAfter downloading the dataset, you need to edit \u003ccode\u003econfig_baseline_multimodal.txt\u003c/code\u003e configuration file (in \u003ccode\u003econfig\u003c/code\u003e folder).\nSet \u003cem\u003edataset_folder\u003c/em\u003e parameter to point to a root folder of PointNetVLAD dataset with 3D point clouds.\n\u003cem\u003eimage_path\u003c/em\u003e parameter must be a folder where downsampled RGB images from Oxford RobotCar dataset will be saved.\nThe folder will be created by \u003ccode\u003egenerate_rgb_for_lidar.py\u003c/code\u003e script.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGenerate training and evaluation tuples\u003c/h4\u003e\u003ca id=\"user-content-generate-training-and-evaluation-tuples\" class=\"anchor\" aria-label=\"Permalink: Generate training and evaluation tuples\" href=\"#generate-training-and-evaluation-tuples\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eRun the below code to generate training pickles (with positive and negative point\nclouds for each anchor point cloud) and evaluation pickles. Training pickle format is optimized and different from the\nformat used in PointNetVLAD code.\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"cd generating_queries/ \n\n# Generate training tuples for the Baseline Dataset\npython generate_training_tuples_baseline.py --dataset_root \u0026lt;dataset_root_path\u0026gt;\n\n# Generate training tuples for the Refined Dataset\npython generate_training_tuples_refine.py --dataset_root \u0026lt;dataset_root_path\u0026gt;\n\n# Generate evaluation tuples\npython generate_test_sets.py --dataset_root \u0026lt;dataset_root_path\u0026gt;\"\u003e\u003cpre lang=\"generate\" class=\"notranslate\"\u003e\u003ccode\u003ecd generating_queries/ \n\n# Generate training tuples for the Baseline Dataset\npython generate_training_tuples_baseline.py --dataset_root \u0026lt;dataset_root_path\u0026gt;\n\n# Generate training tuples for the Refined Dataset\npython generate_training_tuples_refine.py --dataset_root \u0026lt;dataset_root_path\u0026gt;\n\n# Generate evaluation tuples\npython generate_test_sets.py --dataset_root \u0026lt;dataset_root_path\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003e\u0026lt;dataset_root_path\u0026gt;\u003c/code\u003e is a path to dataset root folder, e.g. \u003ccode\u003e/data/pointnetvlad/benchmark_datasets/\u003c/code\u003e.\nBefore running the code, ensure you have read/write rights to \u003ccode\u003e\u0026lt;dataset_root_path\u0026gt;\u003c/code\u003e, as training and evaluation pickles\nare saved there.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDownsample RGB images and index RGB images linked with each point cloud\u003c/h4\u003e\u003ca id=\"user-content-downsample-rgb-images-and-index-rgb-images-linked-with-each-point-cloud\" class=\"anchor\" aria-label=\"Permalink: Downsample RGB images and index RGB images linked with each point cloud\" href=\"#downsample-rgb-images-and-index-rgb-images-linked-with-each-point-cloud\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eRGB images are taken directly from Oxford RobotCar dataset.\nFirst, you need to download stereo camera images from Oxford RobotCar dataset.\nSee dataset website for details (\u003ca href=\"https://robotcar-dataset.robots.ox.ac.uk\" rel=\"nofollow\"\u003elink\u003c/a\u003e).\nAfter downloading Oxford RobotCar dataset, run \u003ccode\u003egenerate_rgb_for_lidar.py\u003c/code\u003e script.\nThe script finds 20 closest RGB images in RobotCar dataset for each 3D point cloud, downsamples them and saves them in the target directory\n(\u003cem\u003eimage_path\u003c/em\u003e parameter in \u003ccode\u003econfig_baseline_multimodal.txt\u003c/code\u003e).\nDuring the training an input to the network consists of a 3D point cloud and one RGB image randomly chosen\nfrom these 20 corresponding images.\nDuring the evaluation, a network input consists of a 3D point cloud and one RGB image with the closest timestamp.\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"cd scripts/ \n\n# Generate training tuples for the Baseline Dataset\npython generate_rgb_for_lidar.py --config ../config/config_baseline_multimodal.txt --oxford_root \u0026lt;path_to_Oxford_RobotCar_dataset\u0026gt;\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003ecd scripts/ \n\n# Generate training tuples for the Baseline Dataset\npython generate_rgb_for_lidar.py --config ../config/config_baseline_multimodal.txt --oxford_root \u0026lt;path_to_Oxford_RobotCar_dataset\u0026gt;\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAlternatively you can download pre-processed and downsampled RobotCar images from this\n\u003ca href=\"https://drive.google.com/file/d/1g6nWvJ-T-M41MyZTa0oRHIwP7G-cxer6/view?usp=sharing\" rel=\"nofollow\"\u003elink\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTraining\u003c/h3\u003e\u003ca id=\"user-content-training\" class=\"anchor\" aria-label=\"Permalink: Training\" href=\"#training\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMinkLoc++\u003c/strong\u003e can be used in unimodal scenario (3D point cloud input only) and multimodal scenario (3D point cloud + RGB image input).\nTo train \u003cstrong\u003eMinkLoc++\u003c/strong\u003e network, download and decompress the 3D point cloud dataset and generate training pickles as described above.\nTo train the multimodal model (3D+RGB) download the original Oxford RobotCar dataset\nand extract RGB images corresponding to 3D point clouds as described above.\nEdit the configuration files:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ccode\u003econfig_baseline_multimodal.txt\u003c/code\u003e when training a multimodal (3D+RGB) model\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003econfig_baseline.txt\u003c/code\u003e and \u003ccode\u003econfig_refined.txt\u003c/code\u003e when train unimodal (3D only) model\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eSet \u003ccode\u003edataset_folder\u003c/code\u003e parameter to the dataset root folder, where 3D point clouds are located.\nSet \u003ccode\u003eimage_path \u003c/code\u003e parameter to the path with RGB images corresponding to 3D point clouds, extracted from\nOxford RobotCar dataset using \u003ccode\u003egenerate_rgb_for_lidar.py\u003c/code\u003e script (only when training a multimodal model).\nModify \u003ccode\u003ebatch_size_limit\u003c/code\u003e parameter depending on the available GPU memory.\nDefault limits requires 11GB of GPU RAM.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo train the multimodal model (3D+RGB), run:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"cd training\n\npython train.py --config ../config/config_baseline_multimodal.txt --model_config ../models/minklocmultimodal.txt\"\u003e\u003cpre lang=\"train\" class=\"notranslate\"\u003e\u003ccode\u003ecd training\n\npython train.py --config ../config/config_baseline_multimodal.txt --model_config ../models/minklocmultimodal.txt\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo train a unimodal model (3D only) model run:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"cd training\n\n# Train unimodal (3D only) model on the Baseline Dataset\npython train.py --config ../config/config_baseline.txt --model_config ../models/minkloc3d.txt\n\n# Train unimodal (3D only) model on the Refined Dataset\npython train.py --config ../config/config_refined.txt --model_config ../models/minkloc3d.txt\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003ecd training\n\n# Train unimodal (3D only) model on the Baseline Dataset\npython train.py --config ../config/config_baseline.txt --model_config ../models/minkloc3d.txt\n\n# Train unimodal (3D only) model on the Refined Dataset\npython train.py --config ../config/config_refined.txt --model_config ../models/minkloc3d.txt\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePre-trained Models\u003c/h3\u003e\u003ca id=\"user-content-pre-trained-models\" class=\"anchor\" aria-label=\"Permalink: Pre-trained Models\" href=\"#pre-trained-models\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePretrained models are available in \u003ccode\u003eweights\u003c/code\u003e directory\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ccode\u003eminkloc_multimodal.pth\u003c/code\u003e multimodal model (3D+RGB) trained on the Baseline Dataset with corresponding RGB images\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eminkloc3d_baseline.pth\u003c/code\u003e unimodal model (3D only) trained on the Baseline Dataset\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eminkloc3d_refined.pth\u003c/code\u003e unimodal model (3D only) trained on the Refined Dataset\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eEvaluation\u003c/h3\u003e\u003ca id=\"user-content-evaluation\" class=\"anchor\" aria-label=\"Permalink: Evaluation\" href=\"#evaluation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo evaluate pretrained models run the following commands:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"cd eval\n\n# To evaluate the multimodal model (3D+RGB only) trained on the Baseline Dataset\npython evaluate.py --config ../config/config_baseline_multimodal.txt --model_config ../models/minklocmultimodal.txt --weights ../weights/minklocmultimodal_baseline.pth\n\n# To evaluate the unimodal model (3D only) trained on the Baseline Dataset\npython evaluate.py --config ../config/config_baseline.txt --model_config ../models/minkloc3d.txt --weights ../weights/minkloc3d_baseline.pth\n\n# To evaluate the unimodal model (3D only) trained on the Refined Dataset\npython evaluate.py --config ../config/config_refined.txt --model_config ../models/minkloc3d.txt --weights ../weights/minkloc3d_refined.pth\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003ecd eval\n\n# To evaluate the multimodal model (3D+RGB only) trained on the Baseline Dataset\npython evaluate.py --config ../config/config_baseline_multimodal.txt --model_config ../models/minklocmultimodal.txt --weights ../weights/minklocmultimodal_baseline.pth\n\n# To evaluate the unimodal model (3D only) trained on the Baseline Dataset\npython evaluate.py --config ../config/config_baseline.txt --model_config ../models/minkloc3d.txt --weights ../weights/minkloc3d_baseline.pth\n\n# To evaluate the unimodal model (3D only) trained on the Refined Dataset\npython evaluate.py --config ../config/config_refined.txt --model_config ../models/minkloc3d.txt --weights ../weights/minkloc3d_refined.pth\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eResults\u003c/h2\u003e\u003ca id=\"user-content-results\" class=\"anchor\" aria-label=\"Permalink: Results\" href=\"#results\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMinkLoc++\u003c/strong\u003e performance (measured by Average Recall@1%) compared to the state of the art:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eMultimodal model (3D+RGB) trained on the Baseline Dataset extended with RGB images\u003c/h3\u003e\u003ca id=\"user-content-multimodal-model-3drgb-trained-on-the-baseline-dataset-extended-with-rgb-images\" class=\"anchor\" aria-label=\"Permalink: Multimodal model (3D+RGB) trained on the Baseline Dataset extended with RGB images\" href=\"#multimodal-model-3drgb-trained-on-the-baseline-dataset-extended-with-rgb-images\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eMethod\u003c/th\u003e\n\u003cth\u003eOxford (AR@1)\u003c/th\u003e\n\u003cth\u003eOxford (AR@1%)\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eCORAL [1]\u003c/td\u003e\n\u003ctd\u003e88.9\u003c/td\u003e\n\u003ctd\u003e96.1\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePIC-Net [2]\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e98.2\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMinkLoc++ (3D+RGB)\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e96.7\u003c/td\u003e\n\u003ctd\u003e99.1\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUnimodal model (3D only) trained on the Baseline Dataset\u003c/h3\u003e\u003ca id=\"user-content-unimodal-model-3d-only-trained-on-the-baseline-dataset\" class=\"anchor\" aria-label=\"Permalink: Unimodal model (3D only) trained on the Baseline Dataset\" href=\"#unimodal-model-3d-only-trained-on-the-baseline-dataset\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eMethod\u003c/th\u003e\n\u003cth\u003eOxford (AR@1%)\u003c/th\u003e\n\u003cth\u003eU.S. (AR@1%)\u003c/th\u003e\n\u003cth\u003eR.A. (AR@1%)\u003c/th\u003e\n\u003cth\u003eB.D (AR@1%)\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ePointNetVLAD [3]\u003c/td\u003e\n\u003ctd\u003e80.3\u003c/td\u003e\n\u003ctd\u003e72.6\u003c/td\u003e\n\u003ctd\u003e60.3\u003c/td\u003e\n\u003ctd\u003e65.3\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePCAN [4]\u003c/td\u003e\n\u003ctd\u003e83.8\u003c/td\u003e\n\u003ctd\u003e79.1\u003c/td\u003e\n\u003ctd\u003e71.2\u003c/td\u003e\n\u003ctd\u003e66.8\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDAGC [5]\u003c/td\u003e\n\u003ctd\u003e87.5\u003c/td\u003e\n\u003ctd\u003e83.5\u003c/td\u003e\n\u003ctd\u003e75.7\u003c/td\u003e\n\u003ctd\u003e71.2\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLPD-Net [6]\u003c/td\u003e\n\u003ctd\u003e94.9\u003c/td\u003e\n\u003ctd\u003e96.0\u003c/td\u003e\n\u003ctd\u003e90.5\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e89.1\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eEPC-Net [7]\u003c/td\u003e\n\u003ctd\u003e94.7\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e96.5\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e88.6\u003c/td\u003e\n\u003ctd\u003e84.9\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSOE-Net [8]\u003c/td\u003e\n\u003ctd\u003e96.4\u003c/td\u003e\n\u003ctd\u003e93.2\u003c/td\u003e\n\u003ctd\u003e91.5\u003c/td\u003e\n\u003ctd\u003e88.5\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNDT-Transformer [10]\u003c/td\u003e\n\u003ctd\u003e97.7\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMinkLoc3D [9]\u003c/td\u003e\n\u003ctd\u003e97.9\u003c/td\u003e\n\u003ctd\u003e95.0\u003c/td\u003e\n\u003ctd\u003e91.2\u003c/td\u003e\n\u003ctd\u003e88.5\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMinkLoc++ (3D-only)\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e98.2\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e94.5\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e92.1\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e88.4\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUnimodal model (3D only) trained on the Refined Dataset\u003c/h3\u003e\u003ca id=\"user-content-unimodal-model-3d-only-trained-on-the-refined-dataset\" class=\"anchor\" aria-label=\"Permalink: Unimodal model (3D only) trained on the Refined Dataset\" href=\"#unimodal-model-3d-only-trained-on-the-refined-dataset\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eMethod\u003c/th\u003e\n\u003cth\u003eOxford (AR@1%)\u003c/th\u003e\n\u003cth\u003eU.S. (AR@1%)\u003c/th\u003e\n\u003cth\u003eR.A. (AR@1%)\u003c/th\u003e\n\u003cth\u003eB.D (AR@1%)\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ePointNetVLAD [3]\u003c/td\u003e\n\u003ctd\u003e80.1\u003c/td\u003e\n\u003ctd\u003e94.5\u003c/td\u003e\n\u003ctd\u003e93.1\u003c/td\u003e\n\u003ctd\u003e86.5\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePCAN [4]\u003c/td\u003e\n\u003ctd\u003e86.4\u003c/td\u003e\n\u003ctd\u003e94.1\u003c/td\u003e\n\u003ctd\u003e92.3\u003c/td\u003e\n\u003ctd\u003e87.0\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDAGC [5]\u003c/td\u003e\n\u003ctd\u003e87.8\u003c/td\u003e\n\u003ctd\u003e94.3\u003c/td\u003e\n\u003ctd\u003e93.4\u003c/td\u003e\n\u003ctd\u003e88.5\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLPD-Net [6]\u003c/td\u003e\n\u003ctd\u003e94.9\u003c/td\u003e\n\u003ctd\u003e98.9\u003c/td\u003e\n\u003ctd\u003e96.4\u003c/td\u003e\n\u003ctd\u003e94.4\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSOE-Net [8]\u003c/td\u003e\n\u003ctd\u003e96.4\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e97.7\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e95.9\u003c/td\u003e\n\u003ctd\u003e92.6\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMinkLoc3D [9]\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e98.5\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e99.7\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e99.3\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e96.7\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMinkLoc++ (RGB-only)\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e98.4\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e99.7\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e99.3\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003e97.4\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003eY. Pan et al., \"CORAL: Colored structural representation for bi-modal place recognition\", preprint arXiv:2011.10934 (2020)\u003c/li\u003e\n\u003cli\u003eY. Lu et al., \"PIC-Net: Point Cloud and Image Collaboration Network for Large-Scale Place Recognition\", preprint arXiv:2008.00658 (2020)\u003c/li\u003e\n\u003cli\u003eM. A. Uy and G. H. Lee, \"PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition\", 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)\u003c/li\u003e\n\u003cli\u003eW. Zhang and C. Xiao, \"PCAN: 3D Attention Map Learning Using Contextual Information for Point Cloud Based Retrieval\", 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)\u003c/li\u003e\n\u003cli\u003eQ. Sun et al., \"DAGC: Employing Dual Attention and Graph Convolution for Point Cloud based Place Recognition\", Proceedings of the 2020 International Conference on Multimedia Retrieval\u003c/li\u003e\n\u003cli\u003eZ. Liu et al., \"LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis\", 2019 IEEE/CVF International Conference on Computer Vision (ICCV)\u003c/li\u003e\n\u003cli\u003eL. Hui et al., \"Efficient 3D Point Cloud Feature Learning for Large-Scale Place Recognition\" preprint arXiv:2101.02374 (2021)\u003c/li\u003e\n\u003cli\u003eY. Xia et al., \"SOE-Net: A Self-Attention and Orientation Encoding Network for Point Cloud based Place Recognition\", 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)\u003c/li\u003e\n\u003cli\u003eJ. Komorowski, \"MinkLoc3D: Point Cloud Based Large-Scale Place Recognition\", Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), (2021)\u003c/li\u003e\n\u003cli\u003eZ. Zhou et al., \"NDT-Transformer: Large-scale 3D Point Cloud Localisation Using the Normal Distribution Transform Representation\",\n2021 IEEE International Conference on Robotics and Automation (ICRA)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJ. Komorowski, M. Wysoczanska, T. Trzcinski, \"MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition\", accepted for International Joint Conference on Neural Networks (IJCNN), (2021)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLicense\u003c/h3\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eOur code is released under the MIT License (see LICENSE file for details).\u003c/p\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition","anchor":"minkloc-lidar-and-monocular-image-fusion-for-place-recognition","htmlText":"MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition"},{"level":3,"text":"Our other projects","anchor":"our-other-projects","htmlText":"Our other projects"},{"level":3,"text":"Introduction","anchor":"introduction","htmlText":"Introduction"},{"level":3,"text":"Citation","anchor":"citation","htmlText":"Citation"},{"level":3,"text":"Environment and Dependencies","anchor":"environment-and-dependencies","htmlText":"Environment and Dependencies"},{"level":3,"text":"Datasets","anchor":"datasets","htmlText":"Datasets"},{"level":4,"text":"Generate training and evaluation tuples","anchor":"generate-training-and-evaluation-tuples","htmlText":"Generate training and evaluation tuples"},{"level":4,"text":"Downsample RGB images and index RGB images linked with each point cloud","anchor":"downsample-rgb-images-and-index-rgb-images-linked-with-each-point-cloud","htmlText":"Downsample RGB images and index RGB images linked with each point cloud"},{"level":3,"text":"Training","anchor":"training","htmlText":"Training"},{"level":3,"text":"Pre-trained Models","anchor":"pre-trained-models","htmlText":"Pre-trained Models"},{"level":3,"text":"Evaluation","anchor":"evaluation","htmlText":"Evaluation"},{"level":2,"text":"Results","anchor":"results","htmlText":"Results"},{"level":3,"text":"Multimodal model (3D+RGB) trained on the Baseline Dataset extended with RGB images","anchor":"multimodal-model-3drgb-trained-on-the-baseline-dataset-extended-with-rgb-images","htmlText":"Multimodal model (3D+RGB) trained on the Baseline Dataset extended with RGB images"},{"level":3,"text":"Unimodal model (3D only) trained on the Baseline Dataset","anchor":"unimodal-model-3d-only-trained-on-the-baseline-dataset","htmlText":"Unimodal model (3D only) trained on the Baseline Dataset"},{"level":3,"text":"Unimodal model (3D only) trained on the Refined Dataset","anchor":"unimodal-model-3d-only-trained-on-the-refined-dataset","htmlText":"Unimodal model (3D only) trained on the Refined Dataset"},{"level":3,"text":"License","anchor":"license","htmlText":"License"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fjac99%2FMinkLocMultimodal"}},{"displayName":"LICENSE","repoName":"MinkLocMultimodal","refName":"master","path":"LICENSE","preferredFileType":"license","tabName":"MIT","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fjac99%2FMinkLocMultimodal"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-7d7eb7c71814.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-708ec8ade250.js","githubDevUrl":null,"enabled_features":{"copilot_workspace":null,"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"accessible_code_button":true,"github_models_repo_integration":false}}}}</script> <div 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0 1 1-2 0 1 1 0 0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg></button></div><div class="Box-sc-g0xbh4-0 QkQOb js-snippet-clipboard-copy-unpositioned undefined" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition</h1><a id="user-content-minkloc-lidar-and-monocular-image-fusion-for-place-recognition" class="anchor" aria-label="Permalink: MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition" href="#minkloc-lidar-and-monocular-image-fusion-for-place-recognition"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Paper: <a href="https://ieeexplore.ieee.org/document/9533373" rel="nofollow">MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition</a> accepted for International Joint Conference on Neural Networks (IJCNN) 2021 <a href="https://arxiv.org/pdf/2104.05327.pdf" rel="nofollow">ArXiv</a></p> <p dir="auto"><a href="mailto:jacek.komorowski@pw.edu.pl">Jacek Komorowski</a>, Monika Wysoczańska, Tomasz Trzciński</p> <p dir="auto">Warsaw University of Technology</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Our other projects</h3><a id="user-content-our-other-projects" class="anchor" aria-label="Permalink: Our other projects" href="#our-other-projects"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>MinkLoc3D: Point Cloud Based Large-Scale Place Recognition (WACV 2021): <a href="https://github.com/jac99/MinkLoc3D">MinkLoc3D</a></li> <li>Large-Scale Topological Radar Localization Using Learned Descriptors (ICONIP 2021): <a href="https://github.com/jac99/RadarLoc">RadarLoc</a></li> <li>EgonNN: Egocentric Neural Network for Point Cloud Based 6DoF Relocalization at the City Scale (IEEE Robotics and Automation Letters April 2022): <a href="https://github.com/jac99/Egonn">EgoNN</a></li> <li>Improving Point Cloud Based Place Recognition with Ranking-based Loss and Large Batch Training (2022): <a href="https://github.com/jac99/MinkLoc3Dv2">MinkLoc3Dv2</a></li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Introduction</h3><a id="user-content-introduction" class="anchor" aria-label="Permalink: Introduction" href="#introduction"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We present a discriminative multimodal descriptor based on a pair of sensor readings: a point cloud from a LiDAR and an image from an RGB camera. Our descriptor, named MinkLoc++, can be used for place recognition, re-localization and loop closure purposes in robotics or autonomous vehicles applications. We use late fusion approach, where each modality is processed separately and fused in the final part of the processing pipeline. The proposed method achieves state-of-the-art performance on standard place recognition benchmarks. We also identify dominating modality problem when training a multimodal descriptor. The problem manifests itself when the network focuses on a modality with a larger overfit to the training data. This drives the loss down during the training but leads to suboptimal performance on the evaluation set. In this work we describe how to detect and mitigate such risk when using a deep metric learning approach to train a multimodal neural network.</p> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="/jac99/MinkLocMultimodal/blob/master/media/overview.jpg"><img src="/jac99/MinkLocMultimodal/raw/master/media/overview.jpg" alt="Overview" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Citation</h3><a id="user-content-citation" class="anchor" aria-label="Permalink: Citation" href="#citation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">If you find this work useful, please consider citing:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@INPROCEEDINGS{9533373, author={Komorowski, Jacek and Wysoczańska, Monika and Trzcinski, Tomasz}, booktitle={2021 International Joint Conference on Neural Networks (IJCNN)}, title={MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition}, year={2021}, doi={10.1109/IJCNN52387.2021.9533373} }"><pre class="notranslate"><code>@INPROCEEDINGS{9533373, author={Komorowski, Jacek and Wysoczańska, Monika and Trzcinski, Tomasz}, booktitle={2021 International Joint Conference on Neural Networks (IJCNN)}, title={MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition}, year={2021}, doi={10.1109/IJCNN52387.2021.9533373} } </code></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Environment and Dependencies</h3><a id="user-content-environment-and-dependencies" class="anchor" aria-label="Permalink: Environment and Dependencies" href="#environment-and-dependencies"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Code was tested using Python 3.8 with PyTorch 1.9.1 and MinkowskiEngine 0.5.4 on Ubuntu 20.04 with CUDA 10.2.</p> <p dir="auto">The following Python packages are required:</p> <ul dir="auto"> <li>PyTorch (version 1.9.1)</li> <li>MinkowskiEngine (version 0.5.4)</li> <li>pytorch_metric_learning (version 1.0 or above)</li> <li>tensorboard</li> <li>colour_demosaicing</li> </ul> <p dir="auto">Modify the <code>PYTHONPATH</code> environment variable to include absolute path to the project root folder:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="export PYTHONPATH=$PYTHONPATH:/home/.../MinkLocMultimodal"><pre lang="export" class="notranslate"><code>export PYTHONPATH=$PYTHONPATH:/home/.../MinkLocMultimodal </code></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Datasets</h3><a id="user-content-datasets" class="anchor" aria-label="Permalink: Datasets" href="#datasets"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><strong>MinkLoc++</strong> is a multimodal descriptor based on a pair of inputs:</p> <ul dir="auto"> <li>a 3D point cloud constructed by aggregating multiple 2D LiDAR scans from Oxford RobotCar dataset,</li> <li>a corresponding RGB image from the stereo-center camera.</li> </ul> <p dir="auto">We use 3D point clouds built by authors of <em>PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition</em> paper (<a href="https://arxiv.org/pdf/1804.03492" rel="nofollow">link</a>). Each point cloud is built by aggregating 2D LiDAR scans gathered during the 20 meter vehicle traversal. For details see PointNetVLAD paper or their github repository (<a href="https://github.com/mikacuy/pointnetvlad">link</a>). You can download training and evaluation point clouds from <a href="https://drive.google.com/open?id=1rflmyfZ1v9cGGH0RL4qXRrKhg-8A-U9q" rel="nofollow">here</a> (<a href="https://drive.google.com/file/d/1-1HA9Etw2PpZ8zHd3cjrfiZa8xzbp41J/view?usp=sharing" rel="nofollow">alternative link</a>).</p> <p dir="auto">After downloading the dataset, you need to edit <code>config_baseline_multimodal.txt</code> configuration file (in <code>config</code> folder). Set <em>dataset_folder</em> parameter to point to a root folder of PointNetVLAD dataset with 3D point clouds. <em>image_path</em> parameter must be a folder where downsampled RGB images from Oxford RobotCar dataset will be saved. The folder will be created by <code>generate_rgb_for_lidar.py</code> script.</p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Generate training and evaluation tuples</h4><a id="user-content-generate-training-and-evaluation-tuples" class="anchor" aria-label="Permalink: Generate training and evaluation tuples" href="#generate-training-and-evaluation-tuples"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Run the below code to generate training pickles (with positive and negative point clouds for each anchor point cloud) and evaluation pickles. Training pickle format is optimized and different from the format used in PointNetVLAD code.</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="cd generating_queries/ # Generate training tuples for the Baseline Dataset python generate_training_tuples_baseline.py --dataset_root <dataset_root_path> # Generate training tuples for the Refined Dataset python generate_training_tuples_refine.py --dataset_root <dataset_root_path> # Generate evaluation tuples python generate_test_sets.py --dataset_root <dataset_root_path>"><pre lang="generate" class="notranslate"><code>cd generating_queries/ # Generate training tuples for the Baseline Dataset python generate_training_tuples_baseline.py --dataset_root <dataset_root_path> # Generate training tuples for the Refined Dataset python generate_training_tuples_refine.py --dataset_root <dataset_root_path> # Generate evaluation tuples python generate_test_sets.py --dataset_root <dataset_root_path> </code></pre></div> <p dir="auto"><code><dataset_root_path></code> is a path to dataset root folder, e.g. <code>/data/pointnetvlad/benchmark_datasets/</code>. Before running the code, ensure you have read/write rights to <code><dataset_root_path></code>, as training and evaluation pickles are saved there.</p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Downsample RGB images and index RGB images linked with each point cloud</h4><a id="user-content-downsample-rgb-images-and-index-rgb-images-linked-with-each-point-cloud" class="anchor" aria-label="Permalink: Downsample RGB images and index RGB images linked with each point cloud" href="#downsample-rgb-images-and-index-rgb-images-linked-with-each-point-cloud"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">RGB images are taken directly from Oxford RobotCar dataset. First, you need to download stereo camera images from Oxford RobotCar dataset. See dataset website for details (<a href="https://robotcar-dataset.robots.ox.ac.uk" rel="nofollow">link</a>). After downloading Oxford RobotCar dataset, run <code>generate_rgb_for_lidar.py</code> script. The script finds 20 closest RGB images in RobotCar dataset for each 3D point cloud, downsamples them and saves them in the target directory (<em>image_path</em> parameter in <code>config_baseline_multimodal.txt</code>). During the training an input to the network consists of a 3D point cloud and one RGB image randomly chosen from these 20 corresponding images. During the evaluation, a network input consists of a 3D point cloud and one RGB image with the closest timestamp.</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="cd scripts/ # Generate training tuples for the Baseline Dataset python generate_rgb_for_lidar.py --config ../config/config_baseline_multimodal.txt --oxford_root <path_to_Oxford_RobotCar_dataset>"><pre class="notranslate"><code>cd scripts/ # Generate training tuples for the Baseline Dataset python generate_rgb_for_lidar.py --config ../config/config_baseline_multimodal.txt --oxford_root <path_to_Oxford_RobotCar_dataset> </code></pre></div> <p dir="auto">Alternatively you can download pre-processed and downsampled RobotCar images from this <a href="https://drive.google.com/file/d/1g6nWvJ-T-M41MyZTa0oRHIwP7G-cxer6/view?usp=sharing" rel="nofollow">link</a>.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Training</h3><a id="user-content-training" class="anchor" aria-label="Permalink: Training" href="#training"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><strong>MinkLoc++</strong> can be used in unimodal scenario (3D point cloud input only) and multimodal scenario (3D point cloud + RGB image input). To train <strong>MinkLoc++</strong> network, download and decompress the 3D point cloud dataset and generate training pickles as described above. To train the multimodal model (3D+RGB) download the original Oxford RobotCar dataset and extract RGB images corresponding to 3D point clouds as described above. Edit the configuration files:</p> <ul dir="auto"> <li><code>config_baseline_multimodal.txt</code> when training a multimodal (3D+RGB) model</li> <li><code>config_baseline.txt</code> and <code>config_refined.txt</code> when train unimodal (3D only) model</li> </ul> <p dir="auto">Set <code>dataset_folder</code> parameter to the dataset root folder, where 3D point clouds are located. Set <code>image_path </code> parameter to the path with RGB images corresponding to 3D point clouds, extracted from Oxford RobotCar dataset using <code>generate_rgb_for_lidar.py</code> script (only when training a multimodal model). Modify <code>batch_size_limit</code> parameter depending on the available GPU memory. Default limits requires 11GB of GPU RAM.</p> <p dir="auto">To train the multimodal model (3D+RGB), run:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="cd training python train.py --config ../config/config_baseline_multimodal.txt --model_config ../models/minklocmultimodal.txt"><pre lang="train" class="notranslate"><code>cd training python train.py --config ../config/config_baseline_multimodal.txt --model_config ../models/minklocmultimodal.txt </code></pre></div> <p dir="auto">To train a unimodal model (3D only) model run:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="cd training # Train unimodal (3D only) model on the Baseline Dataset python train.py --config ../config/config_baseline.txt --model_config ../models/minkloc3d.txt # Train unimodal (3D only) model on the Refined Dataset python train.py --config ../config/config_refined.txt --model_config ../models/minkloc3d.txt"><pre class="notranslate"><code>cd training # Train unimodal (3D only) model on the Baseline Dataset python train.py --config ../config/config_baseline.txt --model_config ../models/minkloc3d.txt # Train unimodal (3D only) model on the Refined Dataset python train.py --config ../config/config_refined.txt --model_config ../models/minkloc3d.txt </code></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Pre-trained Models</h3><a id="user-content-pre-trained-models" class="anchor" aria-label="Permalink: Pre-trained Models" href="#pre-trained-models"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Pretrained models are available in <code>weights</code> directory</p> <ul dir="auto"> <li><code>minkloc_multimodal.pth</code> multimodal model (3D+RGB) trained on the Baseline Dataset with corresponding RGB images</li> <li><code>minkloc3d_baseline.pth</code> unimodal model (3D only) trained on the Baseline Dataset</li> <li><code>minkloc3d_refined.pth</code> unimodal model (3D only) trained on the Refined Dataset</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Evaluation</h3><a id="user-content-evaluation" class="anchor" aria-label="Permalink: Evaluation" href="#evaluation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To evaluate pretrained models run the following commands:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="cd eval # To evaluate the multimodal model (3D+RGB only) trained on the Baseline Dataset python evaluate.py --config ../config/config_baseline_multimodal.txt --model_config ../models/minklocmultimodal.txt --weights ../weights/minklocmultimodal_baseline.pth # To evaluate the unimodal model (3D only) trained on the Baseline Dataset python evaluate.py --config ../config/config_baseline.txt --model_config ../models/minkloc3d.txt --weights ../weights/minkloc3d_baseline.pth # To evaluate the unimodal model (3D only) trained on the Refined Dataset python evaluate.py --config ../config/config_refined.txt --model_config ../models/minkloc3d.txt --weights ../weights/minkloc3d_refined.pth"><pre class="notranslate"><code>cd eval # To evaluate the multimodal model (3D+RGB only) trained on the Baseline Dataset python evaluate.py --config ../config/config_baseline_multimodal.txt --model_config ../models/minklocmultimodal.txt --weights ../weights/minklocmultimodal_baseline.pth # To evaluate the unimodal model (3D only) trained on the Baseline Dataset python evaluate.py --config ../config/config_baseline.txt --model_config ../models/minkloc3d.txt --weights ../weights/minkloc3d_baseline.pth # To evaluate the unimodal model (3D only) trained on the Refined Dataset python evaluate.py --config ../config/config_refined.txt --model_config ../models/minkloc3d.txt --weights ../weights/minkloc3d_refined.pth </code></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Results</h2><a id="user-content-results" class="anchor" aria-label="Permalink: Results" href="#results"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><strong>MinkLoc++</strong> performance (measured by Average Recall@1%) compared to the state of the art:</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Multimodal model (3D+RGB) trained on the Baseline Dataset extended with RGB images</h3><a id="user-content-multimodal-model-3drgb-trained-on-the-baseline-dataset-extended-with-rgb-images" class="anchor" aria-label="Permalink: Multimodal model (3D+RGB) trained on the Baseline Dataset extended with RGB images" href="#multimodal-model-3drgb-trained-on-the-baseline-dataset-extended-with-rgb-images"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <markdown-accessiblity-table><table> <thead> <tr> <th>Method</th> <th>Oxford (AR@1)</th> <th>Oxford (AR@1%)</th> </tr> </thead> <tbody> <tr> <td>CORAL [1]</td> <td>88.9</td> <td>96.1</td> </tr> <tr> <td>PIC-Net [2]</td> <td></td> <td>98.2</td> </tr> <tr> <td><strong>MinkLoc++ (3D+RGB)</strong></td> <td>96.7</td> <td>99.1</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Unimodal model (3D only) trained on the Baseline Dataset</h3><a id="user-content-unimodal-model-3d-only-trained-on-the-baseline-dataset" class="anchor" aria-label="Permalink: Unimodal model (3D only) trained on the Baseline Dataset" href="#unimodal-model-3d-only-trained-on-the-baseline-dataset"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <markdown-accessiblity-table><table> <thead> <tr> <th>Method</th> <th>Oxford (AR@1%)</th> <th>U.S. (AR@1%)</th> <th>R.A. (AR@1%)</th> <th>B.D (AR@1%)</th> </tr> </thead> <tbody> <tr> <td>PointNetVLAD [3]</td> <td>80.3</td> <td>72.6</td> <td>60.3</td> <td>65.3</td> </tr> <tr> <td>PCAN [4]</td> <td>83.8</td> <td>79.1</td> <td>71.2</td> <td>66.8</td> </tr> <tr> <td>DAGC [5]</td> <td>87.5</td> <td>83.5</td> <td>75.7</td> <td>71.2</td> </tr> <tr> <td>LPD-Net [6]</td> <td>94.9</td> <td>96.0</td> <td>90.5</td> <td><strong>89.1</strong></td> </tr> <tr> <td>EPC-Net [7]</td> <td>94.7</td> <td><strong>96.5</strong></td> <td>88.6</td> <td>84.9</td> </tr> <tr> <td>SOE-Net [8]</td> <td>96.4</td> <td>93.2</td> <td>91.5</td> <td>88.5</td> </tr> <tr> <td>NDT-Transformer [10]</td> <td>97.7</td> <td></td> <td></td> <td></td> </tr> <tr> <td>MinkLoc3D [9]</td> <td>97.9</td> <td>95.0</td> <td>91.2</td> <td>88.5</td> </tr> <tr> <td><strong>MinkLoc++ (3D-only)</strong></td> <td><strong>98.2</strong></td> <td>94.5</td> <td><strong>92.1</strong></td> <td>88.4</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Unimodal model (3D only) trained on the Refined Dataset</h3><a id="user-content-unimodal-model-3d-only-trained-on-the-refined-dataset" class="anchor" aria-label="Permalink: Unimodal model (3D only) trained on the Refined Dataset" href="#unimodal-model-3d-only-trained-on-the-refined-dataset"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <markdown-accessiblity-table><table> <thead> <tr> <th>Method</th> <th>Oxford (AR@1%)</th> <th>U.S. (AR@1%)</th> <th>R.A. (AR@1%)</th> <th>B.D (AR@1%)</th> </tr> </thead> <tbody> <tr> <td>PointNetVLAD [3]</td> <td>80.1</td> <td>94.5</td> <td>93.1</td> <td>86.5</td> </tr> <tr> <td>PCAN [4]</td> <td>86.4</td> <td>94.1</td> <td>92.3</td> <td>87.0</td> </tr> <tr> <td>DAGC [5]</td> <td>87.8</td> <td>94.3</td> <td>93.4</td> <td>88.5</td> </tr> <tr> <td>LPD-Net [6]</td> <td>94.9</td> <td>98.9</td> <td>96.4</td> <td>94.4</td> </tr> <tr> <td>SOE-Net [8]</td> <td>96.4</td> <td><strong>97.7</strong></td> <td>95.9</td> <td>92.6</td> </tr> <tr> <td>MinkLoc3D [9]</td> <td><strong>98.5</strong></td> <td><strong>99.7</strong></td> <td><strong>99.3</strong></td> <td>96.7</td> </tr> <tr> <td><strong>MinkLoc++ (RGB-only)</strong></td> <td>98.4</td> <td><strong>99.7</strong></td> <td><strong>99.3</strong></td> <td><strong>97.4</strong></td> </tr> </tbody> </table></markdown-accessiblity-table> <ol dir="auto"> <li>Y. Pan et al., "CORAL: Colored structural representation for bi-modal place recognition", preprint arXiv:2011.10934 (2020)</li> <li>Y. Lu et al., "PIC-Net: Point Cloud and Image Collaboration Network for Large-Scale Place Recognition", preprint arXiv:2008.00658 (2020)</li> <li>M. A. Uy and G. H. Lee, "PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition", 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)</li> <li>W. Zhang and C. Xiao, "PCAN: 3D Attention Map Learning Using Contextual Information for Point Cloud Based Retrieval", 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)</li> <li>Q. Sun et al., "DAGC: Employing Dual Attention and Graph Convolution for Point Cloud based Place Recognition", Proceedings of the 2020 International Conference on Multimedia Retrieval</li> <li>Z. Liu et al., "LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis", 2019 IEEE/CVF International Conference on Computer Vision (ICCV)</li> <li>L. Hui et al., "Efficient 3D Point Cloud Feature Learning for Large-Scale Place Recognition" preprint arXiv:2101.02374 (2021)</li> <li>Y. Xia et al., "SOE-Net: A Self-Attention and Orientation Encoding Network for Point Cloud based Place Recognition", 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)</li> <li>J. Komorowski, "MinkLoc3D: Point Cloud Based Large-Scale Place Recognition", Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), (2021)</li> <li>Z. Zhou et al., "NDT-Transformer: Large-scale 3D Point Cloud Localisation Using the Normal Distribution Transform Representation", 2021 IEEE International Conference on Robotics and Automation (ICRA)</li> </ol> <ul dir="auto"> <li>J. Komorowski, M. Wysoczanska, T. Trzcinski, "MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition", accepted for International Joint Conference on Neural Networks (IJCNN), (2021)</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">License</h3><a id="user-content-license" class="anchor" aria-label="Permalink: License" href="#license"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 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id="site-details-dialog"> <details class="details-reset details-overlay details-overlay-dark lh-default color-fg-default hx_rsm" open> <summary role="button" aria-label="Close dialog"></summary> <details-dialog class="Box Box--overlay d-flex flex-column anim-fade-in fast hx_rsm-dialog hx_rsm-modal"> <button class="Box-btn-octicon m-0 btn-octicon position-absolute right-0 top-0" type="button" aria-label="Close dialog" data-close-dialog> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> <div class="octocat-spinner my-6 js-details-dialog-spinner"></div> </details-dialog> </details> </template> <div class="Popover js-hovercard-content position-absolute" style="display: none; outline: none;"> <div class="Popover-message Popover-message--bottom-left Popover-message--large Box color-shadow-large" style="width:360px;"> </div> </div> <template id="snippet-clipboard-copy-button"> <div class="zeroclipboard-container position-absolute right-0 top-0"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn js-clipboard-copy m-2 p-0" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon m-2"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none m-2"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> <template id="snippet-clipboard-copy-button-unpositioned"> <div class="zeroclipboard-container"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn btn-invisible js-clipboard-copy m-2 p-0 d-flex flex-justify-center flex-items-center" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> </div> <div id="js-global-screen-reader-notice" class="sr-only mt-n1" aria-live="polite" aria-atomic="true" ></div> <div id="js-global-screen-reader-notice-assertive" class="sr-only mt-n1" aria-live="assertive" aria-atomic="true"></div> </body> </html>