Using viewpoint consistency in active stereo vision
James J. Clark, Michael J. Weisman, Alan Yuille · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
Surface models embedded in Bayesian or regularization style stereo vision algorithms bias the solution in a nonviewpoint invariant way. This lack of invariance reveals itself when the surface is computed from different viewpoints. Using the consistency between views one can try to adapt the prior surface models in a way that renders them more viewpoint invariant. The goal is to be able to adapt the stereo algorithm over time so that the same surface shape is obtained from different views. The method described in this paper uses the surface consistency measure to choose between the solutions provided by a set of simple prior surface models.