3D Gaussian Splatting (3DGS) has become an emerging technique with remarkable
potential in 3D representation and image rendering. However, the substantial
storage overhead of 3DGS significantly impedes its practical applications. In
this work, we formulate the compact 3D Gaussian learning as an end-to-end
Rate-Distortion Optimization (RDO) problem and propose RDO-Gaussian that can
achieve flexible and continuous rate control. RDO-Gaussian addresses two main
issues that exist in current schemes: 1) Different from prior endeavors that
minimize the rate under the fixed distortion, we introduce dynamic pruning and
entropy-constrained vector quantization (ECVQ) that optimize the rate and
distortion at the same time. 2) Previous works treat the colors of each
Gaussian equally, while we model the colors of different regions and materials
with learnable numbers of parameters. We verify our method on both real and
synthetic scenes, showcasing that RDO-Gaussian greatly reduces the size of 3D
Gaussian over 40x, and surpasses existing methods in rate-distortion
performance.

该研究提出了 RDO-Gaussian 方法，采用紧凑的 3D 高斯学习作为端到端速率失真优化问题，通过引入动态修剪和熵约束矢量量化来优化速率和失真，从而显著减小 3D 高斯体的大小超过 40 倍，并在速率失真性能上超越了现有方法。