Virtual reality modeling from a sequence of range images

Abstract

Virtual reality object modeling from a sequence of range images has been formulated as a problem of principal component analysis with missing data (PCAMD), which can be generalized as a weighted least square (WLS) minimization problem. An efficient algorithm has been devised to solve the problem of PCAMD. After all visible P regions appeared over the whole sequence of F views are segmented and tracked, a 3F/spl times/P normal measurement matrix of surface normals and an F/spl times/P distance measurement matrix of normal distances to the origin are constructed respectively. These two measurement matrices, with possibly many missing elements due to occlusion and mismatching, enable us to formulate multiple view merging as a combination of two WLS problems. By combining information at both the signal level and the algebraic level, a modified Jarvis' march algorithm is proposed to recover the spatial connectivity among all the reconstructed surface patches. Experiments using synthetic data and real range images show that our approach is robust against noise and mismatch. A toy house model from a sequence of real range images is presented.