Dense Dynamic Scene Reconstruction and Camera Pose Estimation from Multi-View Videos

We address the challenging problem of dense dynamic scenereconstruction and camera pose estimation from multiple freely movingcameras—a setting that arises naturally when multiple observers capturea shared event. Prior approaches either handle only single-camera input orrequire rigidly mounted, pre-calibrated camera rigs, limiting their practicalapplicability. We propose a two-stage optimization framework that decou-ples the task into robust camera tracking and dense depth refinement. Inthe first stage, we extend single-camera visual SLAM to the multi-camerasetting by constructing a spatiotemporal connection graph that exploitsboth intra-camera temporal continuity and inter-camera spatial overlap,enabling consistent scale and robust tracking. To ensure robustness underlimited overlap, we introduce a wide-baseline initialization strategy usingfeed-forward reconstruction models. In the second stage, we refine depthand camera poses by optimizing dense inter- and intra-camera consistencyusing wide-baseline optical flow. Additionally, we introduce MultiCam-Robolab, a new real-world dataset with ground-truth poses from a motioncapture system. Finally, we demonstrate that our method significantly out-performs state-of-the-art feed-forward models on both synthetic and real-world benchmarks, while requiring less memory. Our code is released at