torch-points3d

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.