This repository is a fork of Compact3D (CompGS), accepted to ECCV 2024, where the original diff-gaussian-rasterization backend has been fully replaced by Nerfstudio's GSPLaT rasterizer.
We preserve the full training and evaluation pipeline of Compact3D while leveraging GSPLaT’s modern, CUDA-efficient rasterization backend, which provides:
- ~20% training speedup
- Lower memory usage
- Slight improvements in rendering quality
- Compatibility with OpenGL-style camera & gradient flow
Compact3D is a method to compress large 3D Gaussian Splatting models via vector quantization of Gaussian parameters (color & covariance).
This fork integrates the GSPLaT rasterizer, yielding faster and more memory-efficient training, especially on large scenes.
We use the GSPLaT rasterizer from nerfstudio-project/gsplat.
Please follow the official installation instructions from GSPLaT to set up the rasterization backend and its dependencies:
👉 https://github.com/nerfstudio-project/gsplat#installation
Once installed, make sure the gsplat Python package is available in your environment.
Please follow the official Compact3D documentation for data preparation, training, rendering, and evaluation:
👉 https://github.com/UCDvision/compact3d#readme
The following components have been modified or extended to support gsplat rendering:
- scene/gaussian_model.py
→ Rewrote add_densification_stats() to support absgrad and screen-space scaling based on image size.
- gaussian_renderer/__init__.py
→ Replaced all rasterizer calls with gsplat.rasterization().
- train_kmeans.py
→ Updated training loop to support GSPLaT gradients and max_radii2D image-space scaling.
→ Added memory usage printout (torch.cuda.max_memory_allocated()).
These changes are minimal, focused, and preserve the original Compact3D pipeline and structure.
If you use this repository, please cite:
@article{navaneet2023compact3d,
title={CompGS: Smaller and Faster Gaussian Splatting with Vector Quantization},
author={Navaneet, KL and Meibodi, Kossar Pourahmadi and Koohpayegani, Soroush Abbasi and Pirsiavash, Hamed},
journal={ECCV},
year={2024}
}