A Globally Optimal Approach for 3D Elastic Motion Estimation from Stereo Sequences

Dense and markerless elastic 3D motion estimation based on stereo sequences is a challenge in computer vision. Solutions based on scene flow and 3D registration are mostly restricted to simple non-rigid motions, and suffer from the error accumulation. To address this problem, this paper proposes a globally optimal approach to non-rigid motion estimation which simultaneously recovers the 3D surface as well as its non-rigid motion over time. The instantaneous surface of the object is represented as a set of points which is reconstructed from the matched stereo images, meanwhile its deformation is captured by registering the points over time under spatio-temporal constraints. A global energy is defined on the constraints of stereo, spatial smoothness and temporal continuity, which is optimized via an iterative algorithm to approximate the minimum. Our extensive experiments on real video sequences including different facial expressions, cloth flapping, flag waves, etc. proved the robustness of our method and showed the method effectively handles complex nonrigid motions.