torch-gaussian-splatting-rasterizer

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torch-gaussian-splatting-rasterizer

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Reproducing Gaussian Splatting

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# Minimal Gaussian Splatting Rasterizer
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.

https://github.com/arnaudstiegler/gaussian-splat/assets/26485052/1f08a9c2-f086-40e1-b75e-28317408dc68

## Install

`pip install -r requirements.txt`

Additionally, if you want to generate the video, you will need `ffmpeg` installed.

## How To Run

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]`

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