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× 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× 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.
最近，为了从稀疏图像集合合成新视图，引入了一种优化的3D高斯散点表示进行高保真场景重建。要使这些表示适用于网络流媒体和低功耗设备上的渲染，需要显著降低内存消耗并提高渲染效率。我们提出了一种压缩的3D高斯散点表示，该表示利用敏感度感知的向量聚类和量化感知训练来压缩方向颜色和高斯参数。学习得到的代码本具有低比特率，并在真实世界场景中实现了高达31倍的压缩率，同时仅对视觉质量造成极小的降级。我们展示了压缩的散点表示可以在轻量级GPU上通过硬件光栅化高效渲染，与通过优化的GPU计算管线报告的帧率相比，最高可提高4倍。在多个数据集上的广泛实验展示了所提方法的鲁棒性和渲染速度。