Compressing 3D Gaussian Splatting by Noise-Substituted Vector Quantization
This addresses storage limitations for practical applications of 3D reconstruction, but it is incremental as it builds on existing 3DGS methods.
The paper tackles the high storage cost of 3D Gaussian Splatting for 3D reconstruction by proposing a compression method using noise-substituted vector quantization, reducing memory consumption by around 45 times while maintaining competitive quality.
3D Gaussian Splatting (3DGS) has demonstrated remarkable effectiveness in 3D reconstruction, achieving high-quality results with real-time radiance field rendering. However, a key challenge is the substantial storage cost: reconstructing a single scene typically requires millions of Gaussian splats, each represented by 59 floating-point parameters, resulting in approximately 1 GB of memory. To address this challenge, we propose a compression method by building separate attribute codebooks and storing only discrete code indices. Specifically, we employ noise-substituted vector quantization technique to jointly train the codebooks and model features, ensuring consistency between gradient descent optimization and parameter discretization. Our method reduces the memory consumption efficiently (around $45\times$) while maintaining competitive reconstruction quality on standard 3D benchmark scenes. Experiments on different codebook sizes show the trade-off between compression ratio and image quality. Furthermore, the trained compressed model remains fully compatible with popular 3DGS viewers and enables faster rendering speed, making it well-suited for practical applications.