Temporally Aware Densification for Dynamic 3D Gaussian Splatting
This work addresses the problem of blurry and under-reconstructed dynamic regions in dynamic 3D Gaussian Splatting for computer vision and graphics applications.
Dynamic 3D Gaussian Splatting methods suffer from poor reconstruction of dynamic regions due to static densification strategies. The proposed Visibility-Aware Densification framework with Temporally-Adaptive Thresholding and Temporal Offset Warping achieves substantial improvements in visual quality, outperforming existing methods on three dynamic multi-view benchmarks.
Despite modeling temporal motion, dynamic 3D Gaussian Splatting (3DGS) methods still inherit a static densification strategy that is ill-suited for dynamic scenes. This neglect of temporal behavior leads to under-reconstructed and blurry dynamic regions, as short-lived Gaussians receive sparse supervision and fail to densify effectively. We propose a Visibility-Aware Densification (VAD) framework that integrates temporal visibility into the densification process, ensuring that Gaussians are refined based on their actual temporal presence. A Temporally-Adaptive Thresholding (TAT) mechanism further adjusts each Gaussian's densification threshold according to its temporal lifespan, promoting balanced refinement of both static and dynamic regions. Finally, a Temporal Offset Warping (TOW) design enhances deformation capacity around temporal centers, extending the lifespan of highly dynamic Gaussians and facilitating more effective densification. Our approach achieves substantial improvements in the visual quality of dynamic regions, outperforming existing methods across three dynamic multi-view benchmark datasets. Moreover, the proposed VAD module generalizes across diverse dynamic 3DGS methods, consistently improving dynamic reconstruction as a plug-and-play component.