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Universal Beta Splatting

Rong Liu, Zhongpai Gao, Benjamin Planche, Meida Chen, Van Nguyen Nguyen, Meng Zheng, Anwesa Choudhuri, Terrence Chen, Yue Wang, Andrew Feng, Ziyan Wu

TL;DR

This work introduces Universal Beta Splatting (UBS), a unified framework that generalizes 3D Gaussian Splatting to N-dimensional anisotropic Beta kernels for explicit radiance field rendering and establishes Beta kernels as a scalable universal primitive for radiance field rendering.

Abstract

We introduce Universal Beta Splatting (UBS), a unified framework that generalizes 3D Gaussian Splatting to N-dimensional anisotropic Beta kernels for explicit radiance field rendering. Unlike fixed Gaussian primitives, Beta kernels enable controllable dependency modeling across spatial, angular, and temporal dimensions within a single representation. Our unified approach captures complex light transport effects, handles anisotropic view-dependent appearance, and models scene dynamics without requiring auxiliary networks or specific color encodings. UBS maintains backward compatibility by approximating to Gaussian Splatting as a special case, guaranteeing plug-in usability and lower performance bounds. The learned Beta parameters naturally decompose scene properties into interpretable without explicit supervision: spatial (surface vs. texture), angular (diffuse vs. specular), and temporal (static vs. dynamic). Our CUDA-accelerated implementation achieves real-time rendering while consistently outperforming existing methods across static, view-dependent, and dynamic benchmarks, establishing Beta kernels as a scalable universal primitive for radiance field rendering. Our project website is available at https://rongliu-leo.github.io/universal-beta-splatting/.

Universal Beta Splatting

TL;DR

This work introduces Universal Beta Splatting (UBS), a unified framework that generalizes 3D Gaussian Splatting to N-dimensional anisotropic Beta kernels for explicit radiance field rendering and establishes Beta kernels as a scalable universal primitive for radiance field rendering.

Abstract

We introduce Universal Beta Splatting (UBS), a unified framework that generalizes 3D Gaussian Splatting to N-dimensional anisotropic Beta kernels for explicit radiance field rendering. Unlike fixed Gaussian primitives, Beta kernels enable controllable dependency modeling across spatial, angular, and temporal dimensions within a single representation. Our unified approach captures complex light transport effects, handles anisotropic view-dependent appearance, and models scene dynamics without requiring auxiliary networks or specific color encodings. UBS maintains backward compatibility by approximating to Gaussian Splatting as a special case, guaranteeing plug-in usability and lower performance bounds. The learned Beta parameters naturally decompose scene properties into interpretable without explicit supervision: spatial (surface vs. texture), angular (diffuse vs. specular), and temporal (static vs. dynamic). Our CUDA-accelerated implementation achieves real-time rendering while consistently outperforming existing methods across static, view-dependent, and dynamic benchmarks, establishing Beta kernels as a scalable universal primitive for radiance field rendering. Our project website is available at https://rongliu-leo.github.io/universal-beta-splatting/.

Paper Structure

This paper contains 21 sections, 13 equations, 4 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Preliminary
  4. Method
  5. N-Dimensional Beta Kernel
  6. Spatial-Orthogonal Cholesky Parameterization
  7. Beta-Modulated Conditional Slicing
  8. Universal Compatibility and Interpretability
  9. Optimization and Implementation
  10. Experiments
  11. Experimental Setup
  12. Comparison with the State of the Art
  13. Ablation Study
  14. Conclusion
  15. Comparison with State-of-the-art Methods
  16. ...and 6 more sections

Figures (4)

  • Figure 1: Visualization of UBS rendering quality. For static real-world scenes (left), UBS achieves superior rendering of reflective and specular materials compared to 3DGS 3dgs. For dynamic volumetric scenes (right), UBS maintains high visual fidelity in complex spatio-temporal scenarios where 4DGS yang2023real produces blurring artifacts.
  • Figure 2: Visualization of decomposition. Our learned Beta parameters provide interpretable scene decomposition across both spatial, angular, and temporal dimension without explicit supervision.
  • Figure 3: Qualitative comparison of methods for static and dynamic scenes.
  • Figure 4: Adaptive Beta kernels.