Acquisition of time-varying 3D foot shapes from video
The 3D scanning of time-varying objects based on multi-view geometry has been a hot topic in computer vision in recent years. This paper presents an active approach to acquiring 3D shapes of moving foot from multi-view video sequences based on the explicit marker points woven in socks. The reference 3D model of foot is firstly recovered from the starting frames in the multi-view video clips, and then the markers in each view are traced as a whole based on a continuous motion vector field built from the reference 3D model. The missing vertices in the candidate 3D model caused by self-occlusion, non-uniform illumination and random noises are reliably estimated and reconstructed in 3D space by minimizing the changes of differential features in 3D geometry of the reference model. The experimental results show that our method can robustly recover the 3D model of foot in complex background even if there is a relatively large movement between the adjacent frames, with an acceptable accuracy of the resulting 3D model.
multi-view geometry time-varying shapes 3d foot modeling feature tracking