SCas4D: Structural Cascaded Optimization for Boosting Persistent 4D Novel View Synthesis
This addresses the problem of efficient and accurate dynamic scene modeling for computer vision applications, representing an incremental improvement over existing methods.
The paper tackles the challenge of persistent dynamic scene modeling for tracking and novel-view synthesis by proposing SCas4D, a cascaded optimization framework that leverages structural patterns in 3D Gaussian Splatting. It achieves convergence within 100 iterations per time frame and produces results comparable to existing methods with only one-twentieth of the training iterations.
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.