Compact Neural Appearance Models for Efficient Gaussian Splatting

• URL: http://arxiv.org/abs/2609.05255v1
• Date: Fri, 04 Sep 2026 15:18:54 GMT
• Title: Compact Neural Appearance Models for Efficient Gaussian Splatting
• Abstract summary: primitive-based radiance fields such as 3D Gaussian Splatting typically model view-dependent appearance using low-order spherical harmonics (SH)<n>We present a thorough, end-to-end comparison of SH and recent spherical appearance models.<n>We introduce an implicit alternative that decodes compact per-primitive latent codes using a tiny shared pipeline.
• Score: 1.579851447355843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Explicit primitive-based radiance fields such as 3D Gaussian Splatting typically model view-dependent appearance using low-order spherical harmonics (SH). Although efficient to evaluate, SH coefficients dominate per-primitive storage and memory traffic, while their band-limited basis restricts angular detail. We present a thorough, end-to-end comparison of SH and recent spherical appearance models and introduce an implicit alternative that decodes compact per-primitive latent codes using a tiny shared MLP. We integrate all models into the same optimized pipeline, fusing their forward and backward passes into a differentiable CUDA rasterizer and provide a portable WebGL viewer for laptop and mobile GPUs. Our evaluation across reconstruction quality, memory use, and optimization and rendering performance shows that recent spherical models offer the strongest overall quality-efficiency trade-off. Our neural representation is the most compact model evaluated and, compared to third-degree SH, reduces the per-primitive appearance footprint from 192 to 28 bytes, accelerates optimization by 1.3$\times$, while improving reconstruction quality. We further analyze how appearance parametrization shapes optimization, identifying differences in recovered geometry and the tendency of expressive models to absorb non-static scene content. Together, our framework and analysis provide practical guidance for replacing SH beyond what image metrics alone can capture.

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