Toward Recovering Shape and Motion of 3D Curves from Multi-View Image Sequences

We introduce a framework for recovering the 3D shape and motion of unknown, arbitrarily-moving curves from two or more image sequences acquired simultaneously from distinct points in space. We use this framework to (1) identify ambiguities in the multi-view recovery of (rigid or nonrigid) 3D motion for arbitrary curves, and (2) identify a novel spatio-temporal constraint that couples the problems of 3D shape and 3D motion recovery in the multi-view case. We show that this constraint leads to a simple hypothesizeand-test algorithm for estimating 3D curve shape and motion simultaneously. Experiments performed with synthetic data suggest that, in addition to recovering 3D curve motion, our approach yields shape estimates of higher accuracy than those obtained when stereo analysis alone is applied to a multi-view sequence.