PyTorch3D is a high-performance library for 3D deep learning, providing differentiable rendering, 3D geometry operations, and neural implicit scene representations. The core library offers efficient data structures for meshes, point clouds, and volumes, with packed/padded batching for variable-size inputs. It includes a full differentiable mesh renderer (rasterization, shading, blending), point cloud renderer (including the Pulsar CUDA backend), and an implicit rendering pipeline for NeRF-style models. The library also provides a comprehensive set of 3D operations (KNN, ball query, farthest point sampling, cubify, marching cubes, IoU for 3D boxes, point-mesh distances) and loss functions (chamfer distance, mesh laplacian smoothing, normal consistency). The architecture is modular: cameras, lights, materials, rasterizers, and shaders are composable components. The implicitron subpackage extends this with a configurable framework for novel view synthesis, featuring dataset providers (CO3D, ShapeNet, R2N2), ray samplers, raymarchers, view poolers, and evaluation metrics. Research projects (NeRF, implicitron trainer) demonstrate end-to-end training pipelines with Hydra/OmegaConf configuration. While primarily a library, the repository has strong research characteristics: it includes benchmark scripts, tutorials, and reproduction configs for multiple papers. The codebase is well-tested with over 100 test files, including numerical equivalence tests and benchmarks. Custom CUDA/C++ kernels ensure performance-critical operations are optimized. The library is designed for reuse, with a registry pattern for components, pluggable IO, and a flexible transform system.