Deformable Beta Splatting

Rong Liu, Dylan Sun, Meida Chen, Yue Wang, Andrew Feng

(*Co-first authors, equal technical contribution; Co-advisors, equal leading contribution.)

Abstract: 3D Gaussian Splatting (3DGS) has advanced radiance field reconstruction by enabling real-time rendering. However, its reliance on Gaussian kernels for geometry and low-order Spherical Harmonics (SH) for color encoding limits its ability to capture complex geometries and diverse colors. We introduce Deformable Beta Splatting (DBS), a deformable and compact approach that enhances both geometry and color representation. DBS replaces Gaussian kernels with deformable Beta Kernels, which offer bounded support and adaptive frequency control to capture fine geometric details with higher fidelity while achieving better memory efficiency. In addition, we extended the Beta Kernel to color encoding, which facilitates improved representation of diffuse and specular components, yielding superior results compared to SH-based methods. Furthermore, Unlike prior densification techniques that depend on Gaussian properties, we mathematically prove that adjusting regularized opacity alone ensures distribution-preserved Markov chain Monte Carlo (MCMC), independent of the splatting kernel type. Experimental results demonstrate that DBS achieves state-of-the-art visual quality while utilizing only 45\% of the parameters and rendering 1.5x faster than 3DGS-MCMC, highlighting the superior performance of DBS for real-time radiance field rendering.

New Features

The repo is actively growing. If you see something new, pull it through.

To make sure you're always running the latest and greatest:

bash git pull pip install . # Reinstall to recompile - 05/30/2025: Support the --share_url feature for both train.py and view.py. - 05/26/2025: Support more features for viewer. - 05/25/2025: Support Alpha and Normal maps rendering.

Quickstart

This project is built on top of the Original 3DGS, 3DGS-MCMC and gsplat code bases. The authors are grateful to the original authors for their open-source codebase contributions.

Installation Steps

1. Clone the Repository: sh git clone --single-branch --branch main https://github.com/RongLiu-Leo/beta-splatting.git cd beta-splatting
2. Set Up the Conda Environment: sh conda create -y -n beta_splatting python=3.10 conda activate beta_splatting
3. Install Pytorch (Based on Your CUDA Version) sh pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
4. Install Dependencies and Submodules: sh pip install .

Train a Beta Model

shell python train.py -s <path to COLMAP or NeRF Synthetic dataset>

For example, simply run shell python train.py -s lego --cap_max 300000 --eval You should be able to visualize your first Beta Model like this

Compress a Trained Beta Model

```shell python compress.py --ply

It will produce a folder named "png" for a Beta Model, saving 5x~6x storage compared to a ply file

```

Visualize a Trained Beta Model

```shell python view.py --ply

or

python view.py --png ```

Evaluate a Trained Beta Model

shell python eval.py -s <path to COLMAP or NeRF Synthetic dataset> -m <path to trained model directory>

Produce benchmark results

```shell python benchmark.py -

For example, python benchmark.py -m360

```

| Method | PSNR | SSIM | LPIPS | Num (M) | Size (MB) | Compression Time (s) | Compression Ratio | |-----------------------------|--------|--------|---------|-----------|-------------|------------------------|---------------------| | 3DGS | 27.20 | 0.815 | 0.214 | 3.35 | 752.74 | | 1 | | DBS-full | 28.75 | 0.845 | 0.179 | 3.11 | 356.04 | | 2.11 | | DBS-full (compressed) | 28.60 | 0.840 | 0.182 | 3.11 | 63.48 | 41.70 | 11.86 | | DBS-1M | 28.42 | 0.831 | 0.205 | 1.00 | 114.44 | | 6.58 | | DBS-1M (compressed) | 28.32 | 0.828 | 0.207 | 1.00 | 22.32 | 14.28 | 33.72 | | DBS-490K | 28.03 | 0.816 | 0.232 | 0.49 | 56.08 | | 13.42 | | DBS-490K (compressed) | 27.80 | 0.806 | 0.237 | 0.49 | 11.37 | 9.15 | 66.20 |

Processing your own Scenes

The COLMAP loaders expect the following dataset structure in the source path location:

<location> |---images | |---<image 0> | |---<image 1> | |---... |---sparse |---0 |---cameras.bin |---images.bin |---points3D.bin

For rasterization, the camera models must be either a SIMPLE_PINHOLE or PINHOLE camera. We provide a converter script convert.py, to extract undistorted images and SfM information from input images. Optionally, you can use ImageMagick to resize the undistorted images. This rescaling is similar to MipNeRF360, i.e., it creates images with 1/2, 1/4 and 1/8 the original resolution in corresponding folders. To use them, please first install a recent version of COLMAP (ideally CUDA-powered) and ImageMagick. Put the images you want to use in a directory <location>/input. <location> |---input |---<image 0> |---<image 1> |---... If you have COLMAP and ImageMagick on your system path, you can simply run shell python convert.py -s <location> [--resize] #If not resizing, ImageMagick is not needed

Citation

If you find our code or paper helps, please consider giving us a star or citing: bibtex @misc{liu2025deformablebetasplatting, title={Deformable Beta Splatting}, author={Rong Liu and Dylan Sun and Meida Chen and Yue Wang and Andrew Feng}, year={2025}, eprint={2501.18630}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2501.18630}, }