用锚点结构压缩动态场景重建，速度提升3倍以上
ADC-GS: Anchor-Driven Deformable and Compressed Gaussian Splatting for Dynamic Scene Reconstruction
- 以锚点组织高斯点，分层捕捉不同粒度运动
- 相比传统方法速度提升300%-800%，存储效率最优
- 适合需要高效动态重建的实时应用
现有4D高斯点阵方法依赖每个高斯点从标准空间到目标帧的独立形变，忽略了相邻高斯点间的冗余，导致性能不佳。为此，本文提出锚点驱动的可变形与压缩高斯点阵（ADC-GS），一种紧凑高效的动态场景重建表示。ADC-GS在标准空间中构建基于锚点的高斯点结构，并引入基于时间重要性的锚点优化策略。为减少形变冗余，设计了从粗到细的分层处理流程，以捕捉多粒度运动。此外，采用率失真优化，在码率消耗与表示保真度间取得最佳平衡。实验表明，与逐点形变方法相比，ADC-GS在渲染速度上提升300%-800%，同时实现顶尖的存储效率且不损失渲染质量。代码已开源：https://github.com/H-Huang774/ADC-GS.git。
原文摘要 · Abstract (English)
Existing 4D Gaussian Splatting methods rely on per-Gaussian deformation from a canonical space to target frames, which overlooks redundancy among adjacent Gaussian primitives and results in suboptimal performance. To address this limitation, we propose Anchor-Driven Deformable and Compressed Gaussian Splatting (ADC-GS), a compact and efficient representation for dynamic scene reconstruction. Specifically, ADC-GS organizes Gaussian primitives into an anchor-based structure within the canonical space, enhanced by a temporal significance-based anchor refinement strategy. To reduce deformation redundancy, ADC-GS introduces a hierarchical coarse-to-fine pipeline that captures motions at varying granularities. Moreover, a rate-distortion optimization is adopted to achieve an optimal balance between bitrate consumption and representation fidelity. Experimental results demonstrate that ADC-GS outperforms the per-Gaussian deformation approaches in rendering speed by 300%-800% while achieving state-of-the-art storage efficiency without compromising rendering quality. The code is released at https://github.com/H-Huang774/ADC-GS.git.
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