SymGS : Leveraging Local Symmetries for 3D Gaussian Splatting Compression
This work addresses memory scalability issues in 3D scene rendering for applications like novel view synthesis, though it is incremental as it builds on existing compression methods.
The paper tackles the high memory footprint of 3D Gaussian Splatting by introducing SymGS, a compression framework that leverages local symmetries to eliminate redundant primitives, achieving up to 3x compression over existing methods and an average of 108x compression while preserving rendering quality.
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