发表机构
University of Michigan（密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出QuARC-GS框架，用于在线动态场景重建，通过量化锚定变形和变化门控致密化策略，在保持重建质量与速度的同时，将逐帧存储压缩最多11倍。
AI 中文摘要
神经辐射场（NeRF）和高斯溅射（Gaussian Splatting）等3D场景表示技术在新视角合成方面取得了显著进展，可从任意视角实现高质量渲染。近期这类技术已扩展至动态3D场景，但由于详细场景表示的存储需求大，且重建/渲染速度要求高，实现可持续的在线自由视点视频（FVV）流仍具挑战性，尤其对于较长视频。为应对这些挑战，本文提出量化锚定残差编码高斯流（QuARC-GS），这是一种用于在线动态场景重建的感知量化4D场景优化框架，在保持重建速度和质量的同时实现超高压缩。QuARC-GS用单个规范帧和高度压缩的逐帧残差表示场景，具体通过针对运动、外观和致密化的两种互补策略压缩每个残差。本文引入感知量化锚定变形，在低存储流传输下抑制不重要的运动更新，同时保留有意义的变形，维持重建质量。此外，本文设计了变化门控致密化策略，仅在表现出真实时间变化的区域分配新的高斯，有效消除冗余外观更新并降低存储开销。在广泛使用的数据集上进行的大量实验表明，QuARC-GS实现了有竞争力的重建质量和训练速度，同时与现有最优方法相比，逐帧存储减少了多达11倍。
英文摘要
3D scene representation techniques such as neural radiance fields (NeRFs) and Gaussian splatting have made substantial progress in novel view synthesis, achieving high-quality renderings from arbitrary view angles. More recently, such techniques have been extended to dynamic 3D scenes; however, achieving sustainable online free-viewpoint video (FVV) streaming remains challenging, especially for longer videos, due to significant storage demands of detailed scene representations and high reconstruction/rendering speed needs. To address these challenges, we propose Quantized Anchored Residual Coding Gaussian Streaming (QuARC-GS), a quantization-aware 4D scene optimization framework for online dynamic scene reconstruction that achieves ultra-high compression while maintaining reconstruction speed and quality. QuARC-GS represents a scene using a single canonical frame and highly compressed per-frame residuals. Specifically, we compress each residual through two complementary strategies targeting motion, appearance, and densification. We introduce quantization-aware anchor deformation, which suppresses insignificant motion updates while preserving meaningful deformations, maintaining reconstruction quality under low-storage streaming. Furthermore, we design a change-gated densification strategy that allocates new Gaussians only in regions exhibiting genuine temporal changes, effectively eliminating redundant appearance updates and reducing storage overhead. Extensive experiments on widely used datasets demonstrate that QuARC-GS enables competitive reconstruction quality and training speed while cutting per-frame storage by up to 11$\times$ compared to the state-of-the-art.
Comments9 pages, 5 figures, 3 tables
