We address the problem of estimating the structure and motion of a smooth curved object from its silhouettes observed over time by a trinocular stereo rig under perspective projection. We first construct a model for the local structure along the silhouette for each frame in the temporal sequence. The local models are then integrated into a global surface description by estimating the motion between successive time instants. The algorithm tracks certain surface features (parabolic points) and image features (silhouette inflections and frontier points) which are used to bootstrap the motion estimation process. The entire silhouettes along with the reconstructed local structure are then used to refine the initial motion estimate. We have implemented the proposed approach and report results on real images.
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Artificial Intelligence