提出新方法，让3D重建能同时捕捉静态与动态物体形状。
D$^2$USt3R: Enhancing 3D Reconstruction for Dynamic Scenes
- 直接回归静态与动态对齐的点云，融合时空信息。
- 在多个含复杂运动的数据集上表现优于现有方法。
- 适合需要精确动态场景建模的应用，如自动驾驶。
本文针对动态场景中的3D重建问题，解决传统静态重建方法（如DUSt3R）在动态物体干扰下性能下降的问题。这些方法依赖相机位姿进行对齐，但在动态场景中易失效。为此，我们提出D²USt3R，直接回归静态-动态对齐点云（SDAP），同时捕获静态与动态场景的几何结构。通过显式融合空间与时间信息，该方法实现了稠密3D对应关系，显著提升下游任务性能。大量实验表明，本方法在多个含复杂运动的数据集上均取得更优的3D重建效果。
原文摘要 · Abstract (English)
In this work, we address the task of 3D reconstruction in dynamic scenes, where object motions frequently degrade the quality of previous 3D pointmap regression methods, such as DUSt3R, that are originally designed for static 3D scene reconstruction. Although these methods provide an elegant and powerful solution in static settings, they struggle in the presence of dynamic motions that disrupt alignment based solely on camera poses. To overcome this, we propose $D^2USt3R$ that directly regresses Static-Dynamic Aligned Pointmaps (SDAP) that simultaneiously capture both static and dynamic 3D scene geometry. By explicitly incorporating both spatial and temporal aspects, our approach successfully encapsulates 3D dense correspondence to the proposed pointmaps, enhancing downstream tasks. Extensive experimental evaluations demonstrate that our proposed approach consistently achieves superior 3D reconstruction performance across various datasets featuring complex motions.
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