How to install torch-points3d with MinkowskiEngine backend?

Install MinkowskiEngine separately following its instructions, then install torch-points3d via pip. Ensure CUDA and PyTorch compatibility, and refer to the troubleshooting section for kernel compilation issues if needed.

Torch-points3d vs Open3D for point cloud deep learning?

Torch-points3d is a deep learning framework focused on model training and evaluation for tasks like segmentation, while Open3D is a library for 3D data processing and visualization. Use Torch-points3d for building and benchmarking neural networks, and Open3D for geometric operations and rendering.

How to train a custom model on my own dataset in torch-points3d?

Integrate your dataset by creating a new class in the appropriate task folder (e.g., segmentation), then configure it using Hydra YAML files similar to existing datasets like ScanNet. Adjust the model and training configs to match your data structure.

Does torch-points3d support real-time inference?

While it offers Docker deployment for production inference, the framework is optimized for training and evaluation with deep learning models that may not be real-time optimized. Additional optimization might be required for low-latency applications.

What are the best practices for using mixed precision training?

Enable mixed precision in the training configuration for supported backends like TorchSparse, which reduces memory usage and speeds up training. Ensure you have the latest torchsparse version and set training.enable_mixed=true in your config, as detailed in the README.

How to deploy a torch-points3d model with Docker?

Use the provided Dockerfile and build script to containerize your trained model weights. Run the container with mounted directories for input and output, following the example command in the inference script for forward passes on custom data.

MinkowskiEngine or TorchSparse for sparse convolutions in torch-points3d?

MinkowskiEngine currently shows faster training times and is more stable, while TorchSparse supports mixed precision but is in beta and lacks CPU training. Choose based on your performance needs and hardware compatibility, as noted in the backend support section.

torch-points3d — PyTorch Framework for Point Clouds

What is torch-points3d?

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.

Target Audience

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.

Value Proposition

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.

Use Cases

Best For

Benchmarking state-of-the-art point cloud models on standard datasets
Developing custom deep learning models for 3D semantic segmentation
Reproducing research papers in 3D vision with a unified codebase
Training models for 3D object detection in indoor and outdoor scenes
Experimenting with sparse convolution backends like MinkowskiEngine
Deploying point cloud models to production via Docker containers

Not Ideal For

Projects requiring real-time point cloud inference on edge devices without GPU support
Teams looking for a simple, out-of-the-box solution with minimal configuration and no deep learning
Environments with strict Python versions below 3.7 or no CUDA capabilities
Applications focused solely on 2D computer vision without 3D point cloud analysis

Pros & Cons

Pros

Modular Architecture

Organized by task with reusable components, enabling easy model assembly and customization as shown in the PointNet++ config examples for lean and complex builds.

Multiple Backend Support

Integrates sparse convolution backends like MinkowskiEngine and TorchSparse, allowing efficient 3D convolutions and flexibility in model design for performance optimization.

Pretrained Model Registry

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.

Comprehensive Task Coverage

Supports segmentation, classification, detection, panoptic segmentation, and registration tasks, covering most 3D vision research needs with extensive dataset integration.

Docker Deployment

Provides containerization for easy model deployment and inference in production environments, as detailed in the Docker build script and forward pass examples.

Cons

Complex Setup and Dependencies

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.

Limited Windows Compatibility

Wandb integration is broken on Windows, requiring workarounds like disabling it, and some backends may have platform-specific issues, reducing accessibility.

Beta Backend Limitations

TorchSparse is still in beta and does not support CPU-only training, which restricts development without GPUs and may lead to instability.

Configuration Overhead

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.

Frequently Asked Questions

What is torch-points3d?

Target Audience

Value Proposition

Use Cases

Best For

Benchmarking state-of-the-art point cloud models on standard datasets
Developing custom deep learning models for 3D semantic segmentation
Reproducing research papers in 3D vision with a unified codebase
Training models for 3D object detection in indoor and outdoor scenes
Experimenting with sparse convolution backends like MinkowskiEngine
Deploying point cloud models to production via Docker containers

Not Ideal For

Projects requiring real-time point cloud inference on edge devices without GPU support
Teams looking for a simple, out-of-the-box solution with minimal configuration and no deep learning
Environments with strict Python versions below 3.7 or no CUDA capabilities
Applications focused solely on 2D computer vision without 3D point cloud analysis

Pros & Cons

Pros

Modular Architecture

Organized by task with reusable components, enabling easy model assembly and customization as shown in the PointNet++ config examples for lean and complex builds.

Multiple Backend Support

Integrates sparse convolution backends like MinkowskiEngine and TorchSparse, allowing efficient 3D convolutions and flexibility in model design for performance optimization.

Pretrained Model Registry

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.

Comprehensive Task Coverage

Supports segmentation, classification, detection, panoptic segmentation, and registration tasks, covering most 3D vision research needs with extensive dataset integration.

Docker Deployment

Provides containerization for easy model deployment and inference in production environments, as detailed in the Docker build script and forward pass examples.

Cons

Complex Setup and Dependencies

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.

Limited Windows Compatibility

Wandb integration is broken on Windows, requiring workarounds like disabling it, and some backends may have platform-specific issues, reducing accessibility.

Beta Backend Limitations

TorchSparse is still in beta and does not support CPU-only training, which restricts development without GPUs and may lead to instability.

Configuration Overhead

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.

Frequently Asked Questions

torch-points3d

What is torch-points3d?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

Found a gem we're missing?

torch-points3d

What is torch-points3d?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

Found a gem we're missing?