Deep learning has significantly advanced 2D image recognition, but extending these techniques to 3D data remains underexplored. This paper introduces PyTorch3D, a library designed to address the engineering challenges of 3D deep learning, such as processing heterogeneous data and making graphics operations differentiable. PyTorch3D includes a fast, modular, and efficient differentiable renderer for meshes and point clouds, enabling analysis-by-synthesis approaches. Compared to other differentiable renderers, PyTorch3D is more modular and efficient, allowing for easier extension and scaling to large datasets. The authors demonstrate significant speed and memory improvements and use PyTorch3D to improve the state-of-the-art for unsupervised 3D mesh and point cloud prediction from 2D images on the ShapeNet dataset. PyTorch3D is open-source and aims to accelerate research in 3D deep learning.Deep learning has significantly advanced 2D image recognition, but extending these techniques to 3D data remains underexplored. This paper introduces PyTorch3D, a library designed to address the engineering challenges of 3D deep learning, such as processing heterogeneous data and making graphics operations differentiable. PyTorch3D includes a fast, modular, and efficient differentiable renderer for meshes and point clouds, enabling analysis-by-synthesis approaches. Compared to other differentiable renderers, PyTorch3D is more modular and efficient, allowing for easier extension and scaling to large datasets. The authors demonstrate significant speed and memory improvements and use PyTorch3D to improve the state-of-the-art for unsupervised 3D mesh and point cloud prediction from 2D images on the ShapeNet dataset. PyTorch3D is open-source and aims to accelerate research in 3D deep learning.