A New Approach to Automatic Reconstruction of a 3-D World Using Active Stereo Vision

In this paper, we propose a new automatic approach to reconstructing 3-D environments using an active binocular head. To efficiently store and access the depth estimates, we propose the use of an inverse polar octree which can transform both unbounded depth estimates and unbounded estimation errors into a bounded 3-D space with appropriate resolution. The depth estimates are computed by using the asymptotic Bayesian estimation method. Estimated depth values are then smoothed by using discontinuity-preserving Markov random fields. The path of the local motion required by the asymptotic Bayesian method is determined online automatically to reduce the ambiguity of stereo matching. Rules for checking the consistency between the new observation and the previous observations have been developed to properly update the inverse polar octree. Experimental results showed that the proposed approach is very promising for automatic generation of 3-D models which can be used for rendering a 3-D scene in a virtual reality system.