SymGS：利用局部对称性实现3D高斯泼溅压缩
SymGS : Leveraging Local Symmetries for 3D Gaussian Splatting Compression
AI总结：
针对3D高斯泼溅内存占用高的问题，提出即插即用压缩框架SymGS，通过引入可学习镜像消除反射冗余，可增强现有最优压缩方法，在保留渲染质量的同时实现更高压缩比。
摘要：
3D高斯泼溅（3D Gaussian Splatting）已成为新视角合成领域的变革性技术，核心优势在于高渲染速度与照片级真实感。但其内存占用随场景复杂度快速攀升，通常可达数GB。现有压缩方法通过相似性检测与量化挖掘基元级冗余来解决该问题。本研究引入对称性感知技术突破此类方法的压缩极限，专门针对镜像对称性消除冗余基元，提出新型压缩框架SymGS，在场景中引入可学习镜像，从而消除局部与全局反射冗余以实现压缩。该框架可作为即插即用增强模块，集成到HAC等当前最优压缩方法中实现进一步压缩。与HAC相比，本方法在基准数据集上实现1.66倍压缩，大规模场景下压缩比最高可达3倍。平均而言，SymGS可在保留渲染质量的前提下，对3DGS场景实现108倍压缩，项目页面与补充材料见symgs.github.io。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
