Deformable Beta Splatting

TL;DR

Deformable Beta Splatting introduces a novel deformable kernel-based method that significantly improves geometry and color representation in real-time 3D radiance field rendering, outperforming previous Gaussian-based approaches.

Contribution

The paper proposes Deformable Beta Kernels for enhanced geometry and color encoding, along with a mathematical proof for opacity adjustment ensuring distribution preservation, advancing real-time radiance field rendering.

Findings

Achieves state-of-the-art visual quality.

Uses only 45% of the parameters of previous methods.

Renders 1.5x faster than 3DGS-MCMC.

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. Interactive demonstrations and source code are available on our project website: https://rongliu-leo.github.io/beta-splatting/.