Markov Random Field-Based Clustering for the Integration of Multi-view Range Images

Multi-view range image integration aims at producing a single reasonable 3D point cloud. The point cloud is likely to be inconsistent with the measurements topologically and geometrically due to registration errors and scanning noise. This paper proposes a novel integration method cast in the framework of Markov random fields (MRF). We define a probabilistic description of a MRF model designed to represent not only the interpoint Euclidean distances but also the surface topology and neighbourhood consistency intrinsically embedded in a predefined neighbourhood. Subject to this model, points are clustered in aN iterative manner, which compensates the errors caused by poor registration and scanning noise. The integration is thus robust and experiments show the superiority of our MRF-based approach over existing methods.