In this paper, we tackle the problem of 3D reconstruction of dynamic scenes from multi-view videos. Previous works attempt to model the motion of 3D points in space, which either constrains them to handle a single articulated object or requires extra efforts to handle topology changes. By contrast, we propose to directly estimate the change of Signed Distance Function (SDF), namely SDF flow, of the dynamic scene. We show that the SDF flow captures the evolution of the scene surface and handles topology changes naturally. We further derive the mathematical relation between the SDF flow and the scene flow, which allows us to calculate the scene flow from the SDF flow analytically by solving linear equations. Our experiments on real-world multi-view video datasets show that our reconstructions are better than those of the state-of-the-art methods.
Neural SDF Flow for 3D Reconstruction of Dynamic Scenes
Wei Mao Richard Hartley Mathieu Salzmann miaomiao Liu