Powerful 3D representations such as DUSt3R invariant point maps, which encode 3D shape and camera parameters, have significantly advanced feed forward 3D reconstruction. While point maps assume static scenes, Dynamic Point Maps (DPMs) extend this concept to dynamic 3D content by additionally representing scene motion. However, existing DPMs are limited to image pairs and, like DUSt3R, require post processing via optimization when more than two views are involved. We argue that DPMs are more useful when applied to videos and introduce V-DPM to demonstrate this. First, we show how to formulate DPMs for video input in a way that maximizes representational power, facilitates neural prediction, and enables reuse of pretrained models. Second, we implement these ideas on top of VGGT, a recent and powerful 3D reconstructor. Although VGGT was trained on static scenes, we show that a modest amount of synthetic data is sufficient to adapt it into an effective V-DPM predictor. Our approach achieves state of the art performance in 3D and 4D reconstruction for dynamic scenes. In particular, unlike recent dynamic extensions of VGGT such as P3, DPMs recover not only dynamic depth but also the full 3D motion of every point in the scene.

翻译：强大的3D表示方法（如编码3D形状与相机参数的DUSt3R不变点图）显著推动了前馈式三维重建的发展。虽然点图假设场景是静态的，但动态点图通过额外表征场景运动，将这一概念扩展至动态三维内容。然而，现有动态点图仅限于图像对输入，且与DUSt3R类似，在处理超过两个视角时仍需通过优化进行后处理。我们认为动态点图在应用于视频时将更具实用价值，并为此提出V-DPM。首先，我们展示了如何为视频输入构建动态点图表示，以最大化表征能力、促进神经预测并实现预训练模型复用。其次，我们在近期强大的三维重建框架VGGT基础上实现了这些构想。尽管VGGT在静态场景上训练，但我们证明仅需适量合成数据即可将其适配为高效的V-DPM预测器。该方法在动态场景的三维与四维重建任务中达到了最先进的性能。特别值得注意的是，相较于VGGT的最新动态扩展方法（如P3），动态点图不仅能重建动态深度，还能恢复场景中每个点的完整三维运动轨迹。