We address the challenging problem of dense dynamic scene reconstruction and camera pose estimation from multiple freely moving cameras -- a setting that arises naturally when multiple observers capture a shared event. Prior approaches either handle only single-camera input or require rigidly mounted, pre-calibrated camera rigs, limiting their practical applicability. We propose a two-stage optimization framework that decouples the task into robust camera tracking and dense depth refinement. In the first stage, we extend single-camera visual SLAM to the multi-camera setting by constructing a spatiotemporal connection graph that exploits both intra-camera temporal continuity and inter-camera spatial overlap, enabling consistent scale and robust tracking. To ensure robustness under limited overlap, we introduce a wide-baseline initialization strategy using feed-forward reconstruction models. In the second stage, we refine depth and camera poses by optimizing dense inter- and intra-camera consistency using wide-baseline optical flow. Additionally, we introduce MultiCamRobolab, a new real-world dataset with ground-truth poses from a motion capture system. Finally, we demonstrate that our method significantly outperforms state-of-the-art feed-forward models on both synthetic and real-world benchmarks, while requiring less memory.

翻译：我们解决了从多个自由移动相机进行稠密动态场景重建与相机位姿估计这一具有挑战性的问题——该场景在多个观察者记录同一事件时自然出现。现有方法要么仅能处理单相机输入，要么需要预先标定的刚性固定相机阵列，限制了其实际应用性。我们提出了一种两阶段优化框架，将任务解耦为鲁棒的相机跟踪与稠密深度优化。在第一阶段，我们通过构建一个同时利用相机内时间连续性与相机间空间重叠性的时空连接图，将单相机视觉SLAM扩展至多相机场景，从而实现尺度一致且鲁棒的跟踪。为确保在有限重叠区域下的鲁棒性，我们引入了一种利用前馈重建模型的宽基线初始化策略。在第二阶段，我们通过优化基于宽基线光流的相机间与相机内稠密一致性来优化深度与相机位姿。此外，我们提出了MultiCamRobolab——一个包含运动捕捉系统提供真实位姿的新现实世界数据集。最后，我们证明该方法在合成与真实世界基准测试中均显著优于当前最先进的前馈模型，同时所需内存更少。