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It includes a growing collection of GPU-optimized operations such as modular differentiable rendering, fast conversions between representations, data loading, camera classes, volumetric acceleration data structures, 3D checkpoints, and more.<br></p><div class="separator tablet-45 separator--45"></div><div class="separator product-page tablet-45"></div><div id="i8ak0r-2"><div class="row"><div class="col col-xl-6"><h3 id="i78hmy-2" class="h--smaller">Continuous Additions from NVIDIA Research<br></h3><p id="i2yas-2">Follow library releases for new research components from the NVIDIA Toronto AI Lab and across NVIDIA. Latest releases included <a href="https://kaolin.readthedocs.io/en/latest/modules/kaolin.non_commercial.html#kaolin.non_commercial.FlexiCubes" id="idwaze" target="_blank">FlexiCubes</a>, <a href="https://kaolin.readthedocs.io/en/latest/modules/kaolin.ops.conversions.html#kaolin.ops.conversions.marching_tetrahedra" id="i7yr8l" target="_blank">Deep Marching Tetrahedra</a>, <a href="https://kaolin.readthedocs.io/en/latest/modules/kaolin.ops.mesh.html#kaolin.ops.mesh.subdivide_trianglemesh" id="i4gkfk" target="_blank">differentiable mesh subdivision</a>, and structured point clouds (SPCs) acceleration data structure supporting efficient volumetric rendering.<br></p></div><div class="col"><img alt="Kaolin supports 3D structure and 2D mesh rendering of images" src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/updated_continuous_updates.png" class="img-fluid"><a id="igcw0k-2" href="https://research.nvidia.com/labs/toronto-ai/xcube/" target="_blank"><br></a></div></div></div><div class="separator tablet-45 separator--60"></div><div id="i1dl5i"><div class="row"><div class="col"><img alt="Representation agnostic physics simulation" src="https://developer.download.nvidia.com/images/KaolinPhysicsgif2-optimize.gif" class="img-fluid"><a href="https://research.nvidia.com/labs/toronto-ai/xcube/" target="_blank"><br></a></div><div class="col col-xl-6"><h3 class="h--smaller">Representation Agnostic Physics Simulation<br></h3><p id="iv4jc7">We present a versatile framework for reduced elastic simulations of 3D objects in any geometric representation such as 3D Gaussian Splats, SDFs, point-clouds, and even medical scans. Our mesh-free, grid-free method utilizes implicit neural fields to construct a physics-aware subspace of the object via our data-free training process using the latest Simplicits method.<br></p></div></div></div><div class="separator product-page tablet-45"></div><div class="separator product-page tablet-45"></div><div class="separator tablet-45 separator--60"></div><div id="ix2f1l"><div class="row"><div class="col col-xl-6"><h3 class="h--smaller">Modular Differentiable Rendering<br></h3><p id="illzhp">Develop cutting-edge inverse graphics applications using modular and optimized implementations of differentiable rendering. This includes a <a href="https://kaolin.readthedocs.io/en/latest/notes/differentiable_camera.html" id="ihrmo8" target="_blank">differentiable camera API</a>, a <a href="https://kaolin.readthedocs.io/en/latest/modules/kaolin.render.mesh.html#kaolin.render.mesh.rasterize" id="ilbzg3" target="_blank">mesh differentiable renderer</a> with two rasterization backends, an implementation of <a href="https://kaolin.readthedocs.io/en/latest/modules/kaolin.render.lighting.html#kaolin.render.lighting.SgLightingParameters" id="i9aptt" target="_blank">Spherical Gaussians as environment maps</a> for <a href="https://kaolin.readthedocs.io/en/latest/modules/kaolin.render.lighting.html#kaolin.render.lighting.sg_diffuse_fitted" id="ih6sfm" target="_blank">diffuse</a> and <a href="https://kaolin.readthedocs.io/en/latest/modules/kaolin.render.lighting.html#kaolin.render.lighting.sg_warp_specular_term" id="irzwm5" target="_blank">specular</a> lighting, <a href="https://kaolin.readthedocs.io/en/latest/modules/kaolin.render.mesh.html#kaolin.render.mesh.deftet_sparse_render" id="i7f7uv" target="_blank">DefTet tetrahedral meshes volumetric rendering</a>, and <a href="https://kaolin.readthedocs.io/en/latest/modules/kaolin.render.spc.html" id="i1l044" target="_blank">ray-tracing features for SPCs</a>, allowing both surface and volumetric differentiable rendering. Finally store the materials information in a <a href="https://kaolin.readthedocs.io/en/latest/modules/kaolin.render.materials.html#kaolin.render.materials.PBRMaterial" id="io9g1k" target="_blank">PBRMaterial class</a>.</p></div><div class="col"><img alt="Develop inverse graphics apps with modular differentiable rendering" src="https://developer.download.nvidia.com/images/pipeline.png" class="img-fluid"><div class="separator tablet-45 mt-2"></div><p id="ihyc6o" class="text-center"><a href="https://research.nvidia.com/labs/toronto-ai/DIBRPlus/" id="ifzvrl" target="_blank">DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer</a><br></p><a href="https://research.nvidia.com/labs/toronto-ai/xcube/" target="_blank"><br></a></div></div></div><div class="separator tablet-45 separator--60"></div><div id="iooupf"><div class="row"><div class="col"><img alt="Inspecting a 3D model in Jupyter Notebook" src="https://developer.download.nvidia.com/images/KaolinJup2-optimize.gif" class="img-fluid"><a href="https://research.nvidia.com/labs/toronto-ai/xcube/" target="_blank"><br></a></div><div class="col col-xl-6"><h3 class="h--smaller">Jupyter Notebook 3D Debugging<br></h3><p id="ibx3m8">Many 3D deep learning methods use a custom rendering function, such as implicit representations or custom differentiable renderers. Kaolin Library provides a utility to debug and inspect these 3D renderings in an interactive viewer, directly in a Jupyter notebook, with only a couple lines of code. <br></p></div></div></div><div class="separator tablet-45 separator--60"></div><div id="iezrey"><div class="row"><div class="col col-xl-6"><h3 class="h--smaller">USD Integration in Omniverse<br></h3><p id="il997a">Connecting 3D research to USD content on a live USD stage rendered in NVIDIA Omniverse is easy with Kaolin Library. For example, currently rendered 3D models can be easily converted into PyTorch tensors, ingestible by AI. For a code sample, refer to our <a href="https://research.nvidia.com/labs/toronto-ai/DiffusionTexturePainting/" target="_blank" id="id6s2k">AI Texture Painting</a> extension. <br></p></div><div class="col"><img alt="Painting a winding path in a fantasy house garden using the AI Texture Painting Omniverse extension." src="https://developer.download.nvidia.com/images/ai-texture-painting.png" class="img-fluid"><a href="https://research.nvidia.com/labs/toronto-ai/xcube/" target="_blank"><br></a></div></div></div><div class="separator tablet-45 separator--60"></div><div id="it4pyg"><div class="row"><div class="col"><img alt="Load large 3D datasets to train machine learning models" src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/kaolin/kaolin_003a.png" class="img-fluid"><a href="https://research.nvidia.com/labs/toronto-ai/xcube/" target="_blank"><br></a></div><div class="col col-xl-6"><h3 class="h--smaller">Consistent 3D Data Loading <br></h3><p id="ig1sgf">Load USD, OBJ and glTF 3D formats into a consistent pytorch representation with Kaolin Library import utilities.Easily load large 3D datasets to train machine learning models. <br></p></div></div></div><div class="separator tablet-45 separator--60"></div><div id="iuh6ch"><div class="row"><div class="col col-xl-6"><h3 class="h--smaller">GPU-Optimized 3D Operations<br></h3><p id="itl116">Convert between 3D representations using fast and reliable conversion operations, including <a href="https://kaolin.readthedocs.io/en/latest/modules/kaolin.ops.conversions.html#kaolin.ops.conversions.voxelgrids_to_trianglemeshes" target="_blank" id="ivjwyl">marching cube</a>, <a href="https://kaolin.readthedocs.io/en/latest/modules/kaolin.ops.conversions.html#kaolin.ops.conversions.marching_tetrahedra" target="_blank" id="iqbyxe">marching tetrahedra</a> and <a href="https://kaolin.readthedocs.io/en/latest/modules/kaolin.non_commercial.html#kaolin.non_commercial.FlexiCubes" target="_blank" id="iibaf6">Flexicubes</a>, point cloud sampling from mesh and various conversion to SPCs. Use GPU-optimized implementations of 3D loss functions such as <a href="https://kaolin.readthedocs.io/en/latest/modules/kaolin.metrics.trianglemesh.html#kaolin.metrics.trianglemesh.point_to_mesh_distance" target="_blank" id="iqp4d9">point-to-mesh distance</a>, <a href="https://kaolin.readthedocs.io/en/latest/modules/kaolin.metrics.pointcloud.html#kaolin.metrics.pointcloud.sided_distance" target="_blank" id="ip3siz">nearest point distance</a>, <a href="https://kaolin.readthedocs.io/en/latest/modules/kaolin.metrics.pointcloud.html#kaolin.metrics.pointcloud.chamfer_distance" target="_blank" id="i23a3h">chamfer distance</a>, <a href="https://kaolin.readthedocs.io/en/latest/modules/kaolin.metrics.tetmesh.html#kaolin.metrics.tetmesh.amips" target="_blank" id="iyih2s">AMIPS loss</a>, and a collection of other operations on 3D data, such as <a href="https://kaolin.readthedocs.io/en/latest/modules/kaolin.ops.mesh.html" target="_blank" id="is9dtu">topology processing on mesh</a>, <a href="https://kaolin.readthedocs.io/en/latest/modules/kaolin.ops.voxelgrid.html#kaolin.ops.voxelgrid.extract_odms" target="_blank" id="iwq9qj">extraction</a> and <a href="https://kaolin.readthedocs.io/en/latest/modules/kaolin.ops.voxelgrid.html#kaolin.ops.voxelgrid.project_odms" target="_blank" id="i7sm9h">projection</a> of orthographic depth maps, and operations on SPCs such as <a href="https://kaolin.readthedocs.io/en/latest/modules/kaolin.ops.spc.html#kaolin.ops.spc.Conv3d" target="_blank" id="i7drti">sparse convolutions</a>, <a href="https://kaolin.readthedocs.io/en/latest/modules/kaolin.ops.spc.html#kaolin.ops.spc.unbatched_interpolate_trilinear" target="_blank" id="ilpdth">trilinear interpolation</a>, <a href="https://kaolin.readthedocs.io/en/latest/modules/kaolin.ops.spc.html#kaolin.ops.spc.unbatched_make_dual" target="_blank" id="in9fyb">dual grid creation</a>, and <a href="https://kaolin.readthedocs.io/en/latest/modules/kaolin.ops.conversions.html#kaolin.ops.conversions.unbatched_mesh_to_spc" target="_blank" id="irr8e2">conversion from mesh</a>.<br></p></div><div class="col"><img alt="Kaolin library provides GPU-Optimized 3D conversion operations" src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/kaolin/kaolin_003.png" class="img-fluid"><a href="https://research.nvidia.com/labs/toronto-ai/xcube/" target="_blank"><br></a></div></div></div><div class="separator tablet-45 separator--60"></div><div id="ihhir5"><div class="row"><div class="col"><img alt="Sketch of Camera API" src="https://developer.download.nvidia.com/images/camera-api.png" class="img-fluid"><a href="https://research.nvidia.com/labs/toronto-ai/xcube/" target="_blank"><br></a></div><div class="col col-xl-6"><h3 class="h--smaller">Camera and Mesh API<br></h3><p id="ikslyf">Make use of convenient and modular differentiable <a href="https://kaolin.readthedocs.io/en/latest/notes/differentiable_camera.html" id="itrbf1" target="_blank">Camera API</a>, with many convenience methods and <a href="https://github.com/NVIDIAGameWorks/kaolin/tree/master/examples/recipes/camera" id="iwu68w" target="_blank">recipes</a>. Simplify 3D mesh management in PyTorch with a convenient <a href="https://kaolin.readthedocs.io/en/latest/modules/kaolin.rep.surface_mesh.html#kaolin-rep-surfacemesh" id="i6c14l" target="_blank">mesh class</a>, consistent across imports from different 3D formats, including <a href="https://kaolin.readthedocs.io/en/latest/modules/kaolin.io.gltf.html#kaolin.io.gltf.import_mesh" id="i3wx5d" target="_blank">glTF</a>, <a href="https://kaolin.readthedocs.io/en/latest/modules/kaolin.io.usd.html#kaolin.io.usd.import_mesh" id="ij0zbu" target="_blank">USD</a> and <a href="https://kaolin.readthedocs.io/en/latest/modules/kaolin.io.obj.html#kaolin.io.obj.import_mesh" id="isdf45" target="_blank">OBJ</a>.<br></p></div></div></div></section><div id="iebtn1"></div><section class="page__section pt-0 pb-0"></section><div class="separator product-page"></div><div class="separator tablet-45 separator--60"></div><section class="page__section page__section--light-gray page__last-section page__cta-section"><p id="ihmj8l-3-2-3" class="text-center"><a href="https://github.com/NVIDIAGameWorks/kaolin/" target="_blank" title="Download Now" class="btn btn-cta mt-2 me-4">Download Kaolin Library<br></a><a href="https://kaolin.readthedocs.io/en/latest/" target="_blank" title=" Visit Forums" class="btn btn-cta me-2 mt-2 btn-cta--light">Documentation</a></p><div class="separator product-page"></div></section></main></div><div class="col-xl-1 col-separator"></div><div class="col-xl-2 col-lg-3 col-md-12 col-sm-12 col-sidebar"><aside class="page__sidebar with-sticky-nav"><div class="page-navigation-container"><div class="page-quick-links"><p class="p--small page-quick-links__header">Quick Links</p><ul><li><a href="https://github.com/NVIDIAGameWorks/kaolin/" target="_blank" class="link-cta page-quick-links__link">Download Kaolin Library<br></a></li><li></li><li><a href="https://kaolin.readthedocs.io/en/latest/" target="_blank" class="link-cta page-quick-links__link">Library Documentation</a></li></ul></div><hr><div data-react-class="PageNavigation" data-react-props="{"draggable":"true","editable":"true","id":"ixq0r3"}" data-react-cache-id="PageNavigation-ixq0r3"></div></div></aside></div></div></div><div class="separator separator--90 phone-0"></div></div> </div> <div id='footer' class='mt-auto'></div> <script type="text/javascript"> (() => { const handleQuotesBlock = (quotesBlock, idx) => { const blockquotes = quotesBlock.querySelectorAll('blockquote'); 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