Gaussian Splatting Meets ROS2

Summary

The Online ROS2-Based Gaussian Splatting-Enabled Visualizer Shady Gmira ROSplat is the first online ROS2-based visualizer that leverages Gaussian splatting to render complex 3D scenes. It is designed to efficiently visualize millions of gaussians by using custom ROS2 messages and GPU-accelerated sorting and rendering techniques. ROSplat also supports data loading from PLY files and integrates with ROS2 tools such as bag recording. Setup This project was developed and tested on Ubuntu 24.04 LTS using ROS2 Jazzy. Please note: Performance degrades significantly without an NVIDIA graphics card. Dependencies Mandatory: ROS2 (tested on ROS2 Jazzy) ROS2 (tested on ROS2 Jazzy) Optional (for GPU-based Sorting): cupy (ensure compatibility with your CUDA version) torch (if using PyTorch for GPU sorting) To install the optional GPU-based libraries individually: pip install cupy-cuda12x # Install Cupy (replace 12x with your CUDA version) pip install torch # Install PyTorch The program will automatically prioritize sorting methods in the following order: 1) Torch → 2) Cupy → 3) CPU To install all dependencies at once: pip install -r requirements.txt # For GPU acceleration pip install -r requirements-no-gpu.txt # Without GPU acceleration Docker-Based Setup Alternatively, you can set up the project using Docker. A setup script is available under the docker directory. Before running Docker, ensure you have installed: sudo apt-get install -y nvidia-container-toolkit This enables GPU communication between the host and the container. If you come accross any other issues, follow the instructions under the following link Then, to build and run the Docker container: cd docker ./run_docker.sh -h # Display help and usage instructions ./run_docker.sh -bu # Build the Docker image and launch the container with docker-compose Important: Ensure the host machine's CUDA version matches the version specified in the Dockerfile. If you are using a CUDA version other than 12.6, update the Dockerfile a...