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Recently, high-fidelity scene reconstruction with an optimized 3D Gaussiansplat representation has been introduced for novel view synthesis from sparseimage sets. Making such representations suitable for applications like networkstreaming and rendering on low-power devices requires significantly reducedmemory consumption as well as improved rendering efficiency. We propose acompressed 3D Gaussian splat representation that utilizes sensitivity-awarevector clustering with quantization-aware training to compress directionalcolors and Gaussian parameters. The learned codebooks have low bitrates andachieve a compression rate of up to $31\times$ on real-world scenes with onlyminimal degradation of visual quality. We demonstrate that the compressed splatrepresentation can be efficiently rendered with hardware rasterization onlightweight GPUs at up to $4\times$ higher framerates than reported via anoptimized GPU compute pipeline. Extensive experiments across multiple datasetsdemonstrate the robustness and rendering speed of the proposed approach.

Compressed 3D Gaussian Splatting for Accelerated Novel View Synthesis