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Machine Perception
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></span> </div> </div> <div class="bhoig__breadcrumb-wrapper glue-grid__col--span-10 glue-grid__col--span-9-md glue-grid__col--span-10-lg"> <nav class="glue-breadcrumbs" aria-label="Breadcrumbs"> <ol class="glue-breadcrumbs__list"> <li class="glue-breadcrumbs__item"> <a class="glue-breadcrumbs__link attribution" href="/">Home</a> <svg role="presentation" aria-hidden="true" class="glue-icon "> <use href="/gr/static/assets/icons/glue-icons.svg#chevron-right"></use> </svg> </li> <li class="glue-breadcrumbs__item"> <a class="glue-breadcrumbs__link attribution" href="/research-areas/">Research areas</a> <svg role="presentation" aria-hidden="true" class="glue-icon "> <use href="/gr/static/assets/icons/glue-icons.svg#chevron-right"></use> </svg> </li> </ol> </nav> </div> <h1 class="headline-1 bhoig__headline glue-grid__col--span-10 glue-grid__col--span-9-md glue-grid__col--span-10-lg">Machine Perception</h1> <div class="basic-hero__description bhoig__description glue-grid__col--span-10 glue-grid__col--span-9-md glue-grid__col--span-10-lg"> <p data-block-key="0mlk5">Research in machine perception tackles the hard problems of understanding images, sounds, music and video. In recent years, our computers have become much better at such tasks, enabling a variety of new applications such as: content-based search in Google Photos and Image Search, natural <a href="https://googleresearch.blogspot.com/2015/04/google-handwriting-input-in-82.html">handwriting</a> interfaces for Android, <a href="https://googleresearch.blogspot.com/2015/05/paper-to-digital-in-200-languages.html">optical character recognition</a> for Google Drive documents, and recommendation systems that understand music and YouTube videos. Our approach is driven by algorithms that benefit from processing very large, partially-labeled datasets using parallel computing clusters. A good example is our recent work on object recognition using a novel deep convolutional neural network architecture known as <a href="https://research.google.com/pubs/pub43022.html">Inception</a> that achieves state-of-the-art results on academic benchmarks and allows users to easily search through their large collection of Google Photos. The ability to mine meaningful information from multimedia is broadly applied throughout Google.</p> </div> <div class="bhoig__cta glue-grid__col--span-10 glue-grid__col--span-9-md glue-grid__col--span-10-lg"> </div> </div> </div> </section> <section class="offset-two-up"> <div class="glue-page glue-grid"> <div class="offset-two-up__left-col glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-3-lg"> <h3 class="offset-two-up__headline headline-3">Recent Publications</h3> <div class="offset-two-up__cta-below-description"> <a class="glue-button glue-button--medium-emphasis" href="http://research.google/pubs/?category=machine-perception" > <span class="js-gt-item-id">See More</span> </a> </div> </div> <div class="glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-9-lg"> <div class="publications-list --theme- row-card-list" data-hot-swap="pub-list" data-gt-id="publications_list" data-gt-component-name=""> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/scenefun3d-fine-grained-functionality-and-affordance-understanding-in-3d-scenes/ > SceneFun3D: Fine-Grained Functionality and Affordance Understanding in 3D Scenes </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> Delitzas Alexandros </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Ay莽a Takmaz </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/federicotombari/"> Federico Tombari </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Marc Pollefeys </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Francis Engelmann </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> CVPR (2024) (to appear) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentexisting-3d-scene-understanding-me tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentexisting-3d-scene-understanding-me" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Existing 3D scene understanding methods are heavily focused on 3D semantic and instance segmentation. However, identifying objects and their parts only constitutes an intermediate step towards a more fine-grained goal, which is effectively interacting with the functional interactive elements (e.g., handles, knobs, buttons) in the scene to accomplish diverse tasks. To this end, we introduce SceneFun3D, a large-scale dataset with more than 14.8k highly accurate interaction annotations for 710 high-resolution real-world 3D indoor scenes. We accompany the annotations with motion parameter information, describing how to interact with these elements, and a diverse set of natural language descriptions of tasks that involve manipulating them in the scene context. To showcase the value of our dataset, we introduce three novel tasks, namely functionality segmentation, task-driven affordance grounding and 3D motion estimation, and adapt existing state-of-the-art methods to tackle them. Our experiments show that solving these tasks in real 3D scenes remains challenging despite recent progress in closed-set and open-set 3D scene understanding methods.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/scenefun3d-fine-grained-functionality-and-affordance-understanding-in-3d-scenes/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/hierarchical-text-spotter-for-joint-text-spotting-and-layout-analysis/ > Hierarchical Text Spotter for Joint Text Spotting and Layout Analysis </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/shangbanglong/"> Shangbang Long </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/106618/"> Siyang Qin </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/yasuhisafujii/"> Yasuhisa Fujii </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/alessandrobissacco/"> Alessandro Bissacco </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/107918/"> Michalis Raptis </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Winter Conference on Applications of Computer Vision 2024 (2024) (to appear) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentwe-propose-hierarchical-text-spott tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentwe-propose-hierarchical-text-spott" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">We propose Hierarchical Text Spotter (HTS), the first method for the joint task of word-level text spotting and geometric layout analysis. HTS can annotate text in images with a hierarchical representation of 4 levels: character, word, line, and paragraph. The proposed HTS is characterized by two novel components: (1) a Unified-Detector-Polygon (UDP) that produces Bezier Curve polygons of text lines and an affinity matrix for paragraph grouping between detected lines; (2) a Line-to-Character-to-Word (L2C2W) recognizer that splits lines into characters and further merges them back into words. HTS achieves state-of-the-art results on multiple word-level text spotting benchmark datasets as well as geometric layout analysis tasks. Code will be released upon acceptance.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/hierarchical-text-spotter-for-joint-text-spotting-and-layout-analysis/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/phomoh-implicit-photo-realistic-3d-models-of-human-heads/ > PhoMoH: Implicit Photo-realistic 3D Models of Human Heads </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> Mihai Zanfir </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/107250/"> Thiemo Alldieck </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/cristiansminchisescu/"> Cristian Sminchisescu </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> International Conference on 3D Vision (2024) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentwe-present-phomoh-a-neural-networ tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentwe-present-phomoh-a-neural-networ" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">We present PhoMoH, a neural network methodology to construct generative models of photo-realistic 3D geometry and appearance of human heads including hair, beards, an oral cavity, and clothing. In contrast to prior work, PhoMoH models the human head using neural fields, thus supporting complex topology. Instead of learning a head model from scratch, we propose to augment an existing expressive head model with new features. Concretely, we learn a highly detailed geometry network layered on top of a mid-resolution head model together with a detailed, local geometry-aware, and disentangled color field. Our proposed architecture allows us to learn photo-realistic human head models from relatively little data. The learned generative geometry and appearance networks can be sampled individually and enable the creation of diverse and realistic human heads. Extensive experiments validate our method qualitatively and across different metrics.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/phomoh-implicit-photo-realistic-3d-models-of-human-heads/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/metamix-meta-state-precision-searcher-for-mixed-precision-activation-quantization/ > MetaMix: Meta-state Precision Searcher for Mixed-precision Activation Quantization </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> Han-Byul Kim </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Joo Hyung Lee </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Sungjoo Yoo </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Hong-Seok Kim </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Proc. The 38th Annual AAAI Conference on Artificial Intelligence (AAAI) (2024) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentmixed-precision-quantization-of-ef tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentmixed-precision-quantization-of-ef" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Mixed-precision quantization of efficient networks often suffer from activation instability encountered in the exploration of bit selections. To address this problem, we propose a novel method called MetaMix which consists of bit selection and weight training phases. The bit selection phase iterates two steps, (1) the mixed-precision-aware weight update, and (2) the bit-search training with the fixed mixed-precision-aware weights, both of which combined reduce activation instability in mixed-precision quantization and contribute to fast and high-quality bit selection. The weight training phase exploits the weights and step sizes trained in the bit selection phase and fine-tunes them thereby offering fast training. Our experiments with efficient and hard-to-quantize networks, i.e., MobileNet v2 and v3, and ResNet-18 on ImageNet show that our proposed method pushes the boundary of mixed-precision quantization, in terms of accuracy vs. operations, by outperforming both mixed- and single-precision SOTA methods.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/metamix-meta-state-precision-searcher-for-mixed-precision-activation-quantization/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/textmesh-generation-of-realistic-3d-meshes-from-text-prompts/ > TextMesh: Generation of Realistic 3D Meshes From Text Prompts </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> Christina Tsalicoglou </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Fabian Manhardt </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/106839/"> Alessio Tonioni </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Michael Niemeyer </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/federicotombari/"> Federico Tombari </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> 3DV 2024 (2024) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentthe-ability-to-generate-highly-rea tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentthe-ability-to-generate-highly-rea" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">The ability to generate highly realistic 2D images from mere text prompts has recently made huge progress in terms of speed and quality, thanks to the advent of image diffusion models. Naturally, the question arises if this can be also achieved in the generation of 3D content from such text prompts. To this end, a new line of methods recently emerged trying to harness diffusion models, trained on 2D images, for supervision of 3D model generation using view dependent prompts. While achieving impressive results, these methods, however, have two major drawbacks. First, rather than commonly used 3D meshes, they instead generate neural radiance fields (NeRFs), making them impractical for most real applications. Second, these approaches tend to produce over-saturated models, giving the output a cartoonish looking effect. Therefore, in this work we propose a novel method for generation of highly realistic-looking 3D meshes. To this end, we extend NeRF to employ an SDF backbone, leading to improved 3D mesh extraction. In addition, we propose a novel way to finetune the mesh texture, removing the effect of high saturation and improving the details of the output 3D mesh.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/textmesh-generation-of-realistic-3d-meshes-from-text-prompts/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/lfm-3d-learnable-feature-matching-across-wide-baselines-using-3d-signals/ > LFM-3D: Learnable Feature Matching Across Wide Baselines Using 3D Signals </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> Arjun Karpur </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Guilherme Perrotta </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Ricardo Martin-Brualla </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/howardzhou/"> Howard Zhou </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/andrearaujo/"> Andre Araujo </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Proc. 3DV'24 (2024) (to appear) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentfinding-localized-correspondences tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentfinding-localized-correspondences" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Finding localized correspondences across different images of the same object is crucial to understand its geometry. In recent years, this problem has seen remarkable progress with the advent of deep learning-based local image features and learnable matchers. Still, learnable matchers often underperform when there exists only small regions of co-visibility between image pairs (i.e. wide camera baselines). To address this problem, we leverage recent progress in coarse single-view geometry estimation methods. We propose LFM-3D, a Learnable Feature Matching framework that uses models based on graph neural networks and enhances their capabilities by integrating noisy, estimated 3D signals to boost correspondence estimation. When integrating 3D signals into the matcher model, we show that a suitable positional encoding is critical to effectively make use of the low-dimensional 3D information. We experiment with two different 3D signals - normalized object coordinates and monocular depth estimates - and evaluate our method on large-scale (synthetic and real) datasets containing object-centric image pairs across wide baselines. We observe strong feature matching improvements compared to 2D-only methods, with up to +6% total recall and +28% precision at fixed recall. Additionally, we demonstrate that the resulting improved correspondences lead to much higher relative posing accuracy for in-the-wild image pairs - up to 8.6% compared to the 2D-only approach.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/lfm-3d-learnable-feature-matching-across-wide-baselines-using-3d-signals/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> </div> </div> </div> </section> <section class="offset-two-up --theme-dark both" data-gt-id="offset_two_up" data-gt-component-name=""> <div class="glue-page glue-grid"> <div class="offset-two-up__left-col glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-3-lg"> <h2 class="offset-two-up__headline headline-3">Some of our teams</h2> </div> <div class="glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-9-lg"> <ul class="card-stack--small nested-glue-grid-override" 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