Compact Neural Appearance Models for Efficient Gaussian Splatting

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        Compact Neural Appearance Models for Efficient Gaussian Splatting

arXiv 2026

TL;DR: An efficient appearance framework and a compact neural model for 3DGS.

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×, 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.

Citation

@misc{hahlbohm2026efficientgaussianappearance,
  title         = {Compact Neural Appearance Models for Efficient Gaussian Splatting},
  author        = {Florian Hahlbohm and Jorge Condor and Linus Franke and Martin Eisemann and Marcus Magnor},
  year          = {2026},
  eprint        = {2609.05255},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  url           = {https://arxiv.org/abs/2609.05255},
}

Acknowledgements

We thank Timon Scholz for his contributions to the software infrastructure supporting this work and Jannis Möller for identifying and helping us fix a bug in tiny-cuda-nn. This work was partially funded by the DFG projects “Real-Action VR” (ID 523421583) and “Increasing Realism of Omnidirectional Videos in Virtual Reality” (ID 491805996).

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