A PyTorch framework for deep learning on point clouds, providing a modular and reproducible foundation for 3D vision tasks.
Torch-Points3D is a PyTorch-based framework for deep learning on 3D point clouds, providing tools for training, evaluating, and deploying models across tasks like segmentation, classification, and object detection. It solves the problem of fragmented and non-reproducible research in 3D vision by offering a modular, unified codebase that integrates state-of-the-art models and datasets.
Researchers and developers working on 3D computer vision, particularly those focused on point cloud analysis, semantic segmentation, object detection, and geometric deep learning. It's ideal for teams needing reproducible benchmarks and a flexible foundation for custom model development.
Developers choose Torch-Points3D for its modular design, extensive pre-implemented model zoo, and strong emphasis on reproducibility. It uniquely combines ease of use with the flexibility to build complex architectures, supported by multiple sparse convolution backends and seamless integration with popular 3D datasets.
Pytorch framework for doing deep learning on point clouds.
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Organized by task with reusable components, enabling easy model assembly and customization as shown in the PointNet++ config examples for lean and complex builds.
Integrates sparse convolution backends like MinkowskiEngine and TorchSparse, allowing efficient 3D convolutions and flexibility in model design for performance optimization.
Facilitates sharing and loading of models via WandB with just a few lines of code, streamlining fine-tuning and reproduction with associated transforms and configs.
Supports segmentation, classification, detection, panoptic segmentation, and registration tasks, covering most 3D vision research needs with extensive dataset integration.
Provides containerization for easy model deployment and inference in production environments, as detailed in the Docker build script and forward pass examples.
Requires managing multiple libraries like PyTorch Geometric, Hydra, and optional sparse backends, with troubleshooting needed for CUDA kernel compilation and PyTorch updates, as noted in the README.
Wandb integration is broken on Windows, requiring workarounds like disabling it, and some backends may have platform-specific issues, reducing accessibility.
TorchSparse is still in beta and does not support CPU-only training, which restricts development without GPUs and may lead to instability.
Heavy reliance on Hydra for configuration can be overwhelming for users unfamiliar with YAML files and modular design, adding a learning curve for quick experiments.