Complete 3-D models from video: a global approach

We address the automatic recovery of complete 3-D object models from video streams. Usually, complete 3-D models are built by fusing several depth maps, each computed from a small set of consecutive video frames, using structure from motion techniques. However, since from a small number of similar views, it is very difficult to obtain accurate depth maps, their fusion becomes nontrivial and human interaction is in general required to assemble the complete 3-D model. Instead of using intermediate depth maps, we propose a method to recover complete 3-D models directly from the 2-D motions in the entire set of available video frames. The difficulty that arises when processing long videos is that different regions of the object are seen at different time instants. Our method decides whether a region that has become visible is a region that was seen before, or a previously unseen region, by seeking the simplest rigid object that describes well the observed 2-D motions. This global approach increases significantly the accuracy of the estimates of the 3-D shape of the object and the 3-D motion of the camera. Experiments with artificial data and real video demonstrate the good performance of our method.