Recently, high-fidelity scene reconstruction with an optimized 3D Gaussian
splat representation has been introduced for novel view synthesis from sparse
image sets. Making such representations suitable for applications like network
streaming and rendering on low-power devices requires significantly reduced
memory consumption as well as improved rendering efficiency. We propose a
compressed 3D Gaussian splat representation that utilizes sensitivity-aware
vector clustering with quantization-aware training to compress directional
colors and Gaussian parameters. The learned codebooks have low bitrates and
achieve a compression rate of up to $31\times$ on real-world scenes with only
minimal degradation of visual quality. We demonstrate that the compressed splat
representation can be efficiently rendered with hardware rasterization on
lightweight GPUs at up to $4\times$ higher framerates than reported via an
optimized GPU compute pipeline. Extensive experiments across multiple datasets
demonstrate the robustness and rendering speed of the proposed approach.

通过优化 3D 高斯飞溅表示法，将高保真度场景重建应用于稀疏图像集合的新视角合成，并通过压缩的 3D 高斯飞溅表示法，在显著降低内存消耗和提高渲染效率的基础上，实现了对方向性颜色和高斯参数进行压缩。在多个数据集上进行广泛实验，证明了该方法的鲁棒性和渲染速度。