Compact Neural Appearance Models for Efficient Gaussian Splatting
TL;DR - This paper benchmarks view-dependent appearance models for 3D Gaussian Splatting and introduces compact per-primitive latent codes decoded by a shared MLP. The neural representation cuts appearance storage from 192 to 28 bytes per primitive while improving reconstruction quality and accelerating optimization by 1.3Ă— over third-degree spherical harmonics.
- Compares spherical harmonics, newer spherical models, and the proposed neural representation in one optimized CUDA rasterization pipeline.
- Finds recent spherical models offer the best overall balance between reconstruction quality and efficiency.
- Provides a portable WebGL viewer targeting laptop and mobile GPUs.
- Shows that appearance parameterization affects recovered geometry and how expressive models absorb non-static scene content.