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The README lists data structures for triangle meshes, efficient mesh operations (projective transformations, graph convolution, sampling, loss functions), a differentiable mesh renderer and Implicitron, a framework for new-view synthesis through implicit representations. All operators use PyTorch tensors, handle heterogeneous minibatches, are differentiable and can use GPUs.\n\nUseful for: - Render textured meshes or point clouds differentiably for fitting and optimisation.\n- Optimise camera positions or fit a mesh with texture.\n- Experiment with simple neural radiance fields in the tutorial notebooks.\n\nFirst action: - Follow INSTALL.md for your PyTorch and CUDA setup.\n- Open a tutorial notebook such as deforming a sphere mesh into a dolphin or rendering textured meshes.\n- Read the documentation and the notes on heterogeneous batching, mesh IO and differentiable rendering.\n\nPrerequisites and limits: The README defers requirements to INSTALL.md and gives no versions or hardware figures in the text read.\n\n- It is a research library, not an end-user 3D tool.\n- Installation details depend on your PyTorch and CUDA versions and are in a separate file.\n\nThe README states PyTorch3D is released under the BSD License. Meshes, textures and datasets you use carry their own rights.\n\nWritten from the README text only. 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