PyTorch3D is a library for 3D deep learning that provides efficient, differentiable operators for processing 3D data. It includes a fast, modular, differentiable renderer for meshes and point clouds, enabling analysis-by-synthesis approaches. Compared to other differentiable renderers, PyTorch3D is more modular and efficient, allowing users to easily extend it while also gracefully scaling to large meshes and images. The library is open-source and aims to accelerate research in 3D deep learning by providing reusable data structures for managing batches of point clouds and meshes, and by enabling efficient and differentiable operations on heterogeneous data. The paper introduces PyTorch3D as a solution to the engineering challenges in 3D deep learning, such as efficiently processing heterogeneous data and reframing graphics operations to be differentiable. The library includes a modular and efficient differentiable rendering engine for meshes and point clouds, which allows for analysis-by-synthesis and inverse rendering approaches. The renderer is designed to be modular, efficient, and differentiable, with components that can be easily replaced or customized. PyTorch3D's differentiable renderer is compared with other implementations and demonstrates significant speed and memory improvements. It is used to improve the state-of-the-art for unsupervised 3D mesh and point cloud prediction from 2D images on ShapeNet. The library is also efficient for point cloud rendering, with a modular and efficient point cloud renderer that supports heterogeneous batches of points. The paper presents experiments on unsupervised 3D shape prediction using PyTorch3D, demonstrating superior performance compared to other methods. The results show that PyTorch3D achieves better performance in terms of speed and memory usage, and that its differentiable rendering capabilities enable effective 3D shape prediction from 2D images. The library is also efficient for point cloud prediction, with a model called Point Align that slightly improves shape metrics compared to meshes. Overall, PyTorch3D is a powerful tool for 3D deep learning, providing efficient and differentiable operators for processing 3D data, and enabling the development of new applications in areas such as autonomous vehicles, virtual and augmented reality, and 3D content authoring. The library is open-source and aims to accelerate research in 3D deep learning by providing a flexible and efficient platform for 3D deep learning tasks.PyTorch3D is a library for 3D deep learning that provides efficient, differentiable operators for processing 3D data. It includes a fast, modular, differentiable renderer for meshes and point clouds, enabling analysis-by-synthesis approaches. Compared to other differentiable renderers, PyTorch3D is more modular and efficient, allowing users to easily extend it while also gracefully scaling to large meshes and images. The library is open-source and aims to accelerate research in 3D deep learning by providing reusable data structures for managing batches of point clouds and meshes, and by enabling efficient and differentiable operations on heterogeneous data. The paper introduces PyTorch3D as a solution to the engineering challenges in 3D deep learning, such as efficiently processing heterogeneous data and reframing graphics operations to be differentiable. The library includes a modular and efficient differentiable rendering engine for meshes and point clouds, which allows for analysis-by-synthesis and inverse rendering approaches. The renderer is designed to be modular, efficient, and differentiable, with components that can be easily replaced or customized. PyTorch3D's differentiable renderer is compared with other implementations and demonstrates significant speed and memory improvements. It is used to improve the state-of-the-art for unsupervised 3D mesh and point cloud prediction from 2D images on ShapeNet. The library is also efficient for point cloud rendering, with a modular and efficient point cloud renderer that supports heterogeneous batches of points. The paper presents experiments on unsupervised 3D shape prediction using PyTorch3D, demonstrating superior performance compared to other methods. The results show that PyTorch3D achieves better performance in terms of speed and memory usage, and that its differentiable rendering capabilities enable effective 3D shape prediction from 2D images. The library is also efficient for point cloud prediction, with a model called Point Align that slightly improves shape metrics compared to meshes. Overall, PyTorch3D is a powerful tool for 3D deep learning, providing efficient and differentiable operators for processing 3D data, and enabling the development of new applications in areas such as autonomous vehicles, virtual and augmented reality, and 3D content authoring. The library is open-source and aims to accelerate research in 3D deep learning by providing a flexible and efficient platform for 3D deep learning tasks.