利用对称性压缩3D高斯点云，实现超高压缩比。
SymGS : Leveraging Local Symmetries for 3D Gaussian Splatting Compression
- 引入可学习镜面，识别并消除局部与全局反射冗余。
- 相比HAC方法平均压缩108倍，大场景最高达3倍。
- 可无缝集成现有压缩框架，适合大规模场景优化。
3D高斯点云渲染技术在新视角合成中表现出色，但内存占用随场景复杂度急剧上升，常达数GB。现有压缩方法通过检测相似性与量化来减少原始数据冗余。本文提出一种新框架SymGS，通过引入可学习镜面，系统性消除局部和全局反射对称带来的冗余，从而突破传统方法的压缩极限。该框架作为即插即用模块，可与先进压缩方法（如HAC）结合使用。在基准数据集上，相比HAC实现1.66倍压缩（大场景最高达3倍），平均可实现108倍压缩，同时保持高质量渲染效果。项目主页及补充材料见symgs.github.io。
原文摘要 · Abstract (English)
3D Gaussian Splatting has emerged as a transformative technique in novel view synthesis, primarily due to its high rendering speed and photorealistic fidelity. However, its memory footprint scales rapidly with scene complexity, often reaching several gigabytes. Existing methods address this issue by introducing compression strategies that exploit primitive-level redundancy through similarity detection and quantization. We aim to surpass the compression limits of such methods by incorporating symmetry-aware techniques, specifically targeting mirror symmetries to eliminate redundant primitives. We propose a novel compression framework, SymGS, introducing learnable mirrors into the scene, thereby eliminating local and global reflective redundancies for compression. Our framework functions as a plug-and-play enhancement to state-of-the-art compression methods, (e.g. HAC) to achieve further compression. Compared to HAC, we achieve $1.66 \times$ compression across benchmark datasets (upto $3\times$ on large-scale scenes). On an average, SymGS enables $\bf{108\times}$ compression of a 3DGS scene, while preserving rendering quality. The project page and supplementary can be found at symgs.github.io
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