Deformable Beta Splatting (DBS) enhances real-time radiance field rendering by introducing deformable Beta Kernels for superior geometric fidelity, Spherical Beta for efficient color encoding, and kernel-agnostic MCMC optimization, achieving state-of-the-art visual quality with 45% fewer parameters and 1.5x faster rendering than 3DGS-MCMC.
Representation Learning, Efficiency, Multimodality, Generative Modeling
Rong Liu, Dylan Sun, Meida Chen, Yue Wang, Andrew Feng
University of Southern California, Institute for Creative Technologies
Generated by grok-3
Background Problem
The research addresses limitations in 3D Gaussian Splatting (3DGS), a method for real-time radiance field rendering that, while efficient, struggles with capturing fine geometric details and complex view-dependent colors due to the smoothing effect of Gaussian kernels and the parameter-heavy low-order Spherical Harmonics (SH) for color encoding. The key problem solved is enhancing both geometric fidelity and color representation in real-time rendering, overcoming the inherent constraints of fixed-function mappings in Gaussian kernels and SH, which fail to adapt to diverse scene complexities and lighting conditions, thus improving visual quality over 3DGS and competing with state-of-the-art Neural Radiance Fields (NeRF) methods.
Method
Deformable Beta Splatting (DBS) introduces three core innovations for radiance field rendering:
- Beta Kernel for Geometry Representation: Inspired by the Beta distribution, this kernel replaces Gaussian kernels with a bounded, deformable function parameterized by a shape control parameter , allowing adaptive representation of flat surfaces, sharp edges, and smooth regions. It starts with a Gaussian-like shape () and evolves during optimization to capture diverse geometries with minimal memory overhead.
- Spherical Beta (SB) for Color Encoding: This method extends the Beta Kernel to view-dependent color modeling, decoupling diffuse and specular components inspired by the Phong Reflection Model. It uses fewer parameters (linear scaling with lobes) compared to SH (quadratic scaling), enabling sharp specular highlights and high-frequency lighting effects.
- Kernel-Agnostic Markov Chain Monte Carlo (MCMC) Optimization: Building on 3DGS-MCMC, DBS proves mathematically that adjusting regularized opacity alone preserves distribution during densification, regardless of kernel type or clone count, simplifying optimization by eliminating complex scale adjustments. A Beta-based noise function is also introduced to maintain consistency and encourage exploration during training.
Experiment
The experiments evaluate DBS on multiple datasets including Mip-NeRF 360, Tanks and Temples, Deep Blending, and NeRF Synthetic, using metrics like PSNR, SSIM, and LPIPS. The setup compares DBS against state-of-the-art implicit (Zip-NeRF) and explicit (3DGS-MCMC) methods, with initialization using Colmap SfM point clouds or random primitives. Results show DBS outperforming competitors in visual quality across nearly all datasets, with PSNR gains (e.g., 28.75 vs. 28.29 on Mip-NeRF 360 for DBS-full vs. 3DGS-MCMC) and rendering 1.5x faster (123.09 FPS vs. 82.46 FPS for 3DGS-MCMC). It also uses only 45% of the parameters (356.04 MB vs. 733.19 MB). The experimental design is comprehensive, covering diverse scenes and including ablation studies on kernel type, Spherical Beta lobes, and opacity regularization. However, the results might be overly optimistic due to potential tuning for specific datasets, and limitations like popping artifacts are noted but not quantified. The efficiency claims match expectations, though generalizability to untested scenarios remains a concern.
Further Thoughts
The introduction of deformable Beta Kernels in DBS opens up intriguing possibilities for broader applications beyond radiance field rendering, such as in generative modeling for 3D content creation where adaptive geometry representation could enhance realism in synthetic data generation. The kernel-agnostic MCMC approach also suggests potential cross-pollination with other optimization-heavy fields like reinforcement learning for real-time control tasks, where distribution-preserving strategies could stabilize training in dynamic environments. However, a critical concern is whether the Beta Kernel’s adaptability might lead to overfitting on training data distributions, especially in less constrained real-world scenarios not covered by the tested datasets. Comparing this work to recent advancements in diffusion models for 3D generation, such as those leveraging latent representations, could reveal whether combining DBS’s explicit kernel approach with implicit latent spaces might yield even more compact and flexible scene representations. This intersection could be a fruitful area for future research, potentially addressing the noted limitations in handling mirror-like reflections and anisotropic effects.