# Dynamic Scenes

MegaSaM is a system that allows for accurate, rapid, and robust estimation of camera parameters and depth maps from monocular videos of dynamic scenes. This system overcomes the limitations of traditional structure-from-motion and monocular SLAM techniques, which typically assume that the input videos primarily contain static scenes with significant parallax. MegaSaM can be extended to videos of complex dynamic scenes in the real world, including those with unknown fields of view and unconstrained camera paths, through carefully modified depth-visual SLAM frameworks. Extensive experiments on both synthetic and real videos demonstrate that MegaSaM is more accurate and robust in camera pose and depth estimation while being faster or comparable in runtime to previous and concurrent work.

OmniRe is a comprehensive method for efficiently reconstructing high-fidelity dynamic urban scenes from device logs. This technology achieves a complete reconstruction of different objects in the scene by constructing a dynamic neural scene graph based on Gaussian representations and building multiple local canonical spaces to simulate various dynamic actors, including vehicles, pedestrians, and cyclists. OmniRe enables comprehensive reconstruction of different objects present in a scene, allowing for real-time simulation of reconstructed scenes involving all participants. Extensive evaluations on the Waymo dataset show that OmniRe significantly outperforms previous state-of-the-art methods both quantitatively and qualitatively.