This is a torch implementation of the rasterization process for Gaussian Splatting.
Custom CUDA kernels are re-implemented in Torch, thereby loosing the CUDA parallelism. As a result, the implementation rasterizes Gaussians one at a time rather than distributing the rasterization per pixel (and parallelizing it with custom CUDA kernels). It is of course pretty suboptimal and not meant to be used for training/evaluation: rendering with this codebase will take about 5 minutes / image vs. less than a second for the original implementation.
render_video.mp4
pip install -r requirements.txt
Additionally, if you want to generate the video, you will need ffmpeg installed.
MipNerf 360 scenes can be found at: https://jonbarron.info/mipnerf360/
Trained Gaussian Splatting models can be found at: https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/datasets/pretrained/models.zip
To try out the rasterization, run the following:
python rasterize.py --input_dir {MIPNERF_360_PATH} --trained_model_path {GAUSSIAN_MODEL_PATH} --output_path {OUTPUT_PATH} --scene-index {SCENE_INDEX} --scale-factor 2 [--generate_video]