CSGaussian：渐进率失真压缩与分割用于3D高斯溅射
CSGaussian: Progressive Rate-Distortion Compression and Segmentation for 3D Gaussian Splatting
AI总结：
CSGaussian提出一种统一框架，通过整合语义学习实现3D高斯溅射的率失真优化压缩与分割，提升场景编辑与操作能力。
摘要：
我们提出了首个统一的框架，用于优化率失真压缩和分割3D高斯溅射（3DGS）。虽然3DGS在实时渲染和语义场景理解中已证明有效，但先前的工作大多将这些任务独立处理，未探索其联合考虑。受近期率失真优化3DGS压缩的进展启发，本工作将语义学习整合到压缩流程中，以支持解码器端应用——如场景编辑和操作——这些应用超出了传统场景重建和视图合成的范围。我们的方案特征一个轻量级隐式神经表示的超先验，使颜色和语义属性的高效熵编码成为可能，同时避免了许多先前工作中昂贵的网格基超先验。为了促进压缩和分割，我们进一步开发了压缩引导的分割学习，包括量化感知训练以增强特征分离性，以及质量感知加权机制以抑制不可靠的高斯原始体。在LERF和3D-OVS数据集上的广泛实验表明，我们的方法显著降低了传输成本，同时保持高质量的渲染效果和强大的分割性能。
英文摘要：
We present the first unified framework for rate-distortion-optimized compression and segmentation of 3D Gaussian Splatting (3DGS). While 3DGS has proven effective for both real-time rendering and semantic scene understanding, prior works have largely treated these tasks independently, leaving their joint consideration unexplored. Inspired by recent advances in rate-distortion-optimized 3DGS compression, this work integrates semantic learning into the compression pipeline to support decoder-side applications--such as scene editing and manipulation--that extend beyond traditional scene reconstruction and view synthesis. Our scheme features a lightweight implicit neural representation-based hyperprior, enabling efficient entropy coding of both color and semantic attributes while avoiding costly grid-based hyperprior as seen in many prior works. To facilitate compression and segmentation, we further develop compression-guided segmentation learning, consisting of quantization-aware training to enhance feature separability and a quality-aware weighting mechanism to suppress unreliable Gaussian primitives. Extensive experiments on the LERF and 3D-OVS datasets demonstrate that our approach significantly reduces transmission cost while preserving high rendering quality and strong segmentation performance.
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