通过分层优化提升动态场景4D新视角生成效率与精度
SCas4D: Structural Cascaded Optimization for Boosting Persistent 4D Novel View Synthesis
- 基于3D高斯点云的层级变形优化，分阶段从粗到细精修
- 每帧仅需100次迭代即可收敛，训练迭代次数仅为原有方法1/20
- 适用于自监督关节物体分割、新视角合成与密集点追踪
持续动态场景建模在跟踪与新视角合成方面仍具挑战，主要难点在于准确捕捉形变的同时保持计算效率。本文提出SCas4D，一种基于3D高斯溅射中结构模式的级联优化框架。核心思想是真实世界形变常呈现层次性，即一组高斯点共享相似变换。通过从粗粒度部件级逐步优化至精细点级，SCas4D在每帧仅需100次迭代即可收敛，且结果媲美现有方法，训练迭代次数仅为原方法的二十分之一。该方法在自监督关节物体分割、新视角合成及密集点追踪任务中均表现出色。
原文摘要 · Abstract (English)
Persistent dynamic scene modeling for tracking and novel-view synthesis remains challenging due to the difficulty of capturing accurate deformations while maintaining computational efficiency. We propose SCas4D, a cascaded optimization framework that leverages structural patterns in 3D Gaussian Splatting for dynamic scenes. The key idea is that real-world deformations often exhibit hierarchical patterns, where groups of Gaussians share similar transformations. By progressively refining deformations from coarse part-level to fine point-level, SCas4D achieves convergence within 100 iterations per time frame and produces results comparable to existing methods with only one-twentieth of the training iterations. The approach also demonstrates effectiveness in self-supervised articulated object segmentation, novel view synthesis, and dense point tracking tasks.
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