General Info

This repo contains modification of the original differential Gaussian rasterizer. The main difference are the depth backpropagation, and alpha rendering. Also, we provide some test data and cmake file which allows debugging the CUDA code itself.

Install

This command will install the repo to a python project:

pip install .

Build and debug

If you want to debug the cuda code with the debugger, we provide some test data for it.

Download test data

cd test_data
git clone https://huggingface.co/datasets/voviktyl/gaussian-rasterizer-test-data

All the test data was obtained from Replica dataset. Adjust the input paths and process the downloaded dump files with the command:

python convert_dump.py

This would unpack the dump files to .pt tensors.

Build the project

First, adjust the paths cmake file. After that run:

mkdir build
cd build
cmake ..
make

This would create rasterizer executable which can be examined with the VSCode debugger.

BibTeX

@Article{kerbl3Dgaussians,
      author       = {Kerbl, Bernhard and Kopanas, Georgios and Leimk{\"u}hler, Thomas and Drettakis, George},
      title        = {3D Gaussian Splatting for Real-Time Radiance Field Rendering},
      journal      = {ACM Transactions on Graphics},
      number       = {4},
      volume       = {42},
      month        = {July},
      year         = {2023},
      url          = {https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/}
}