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GENERATIVE MODELS AND MARKOV CHAIN MONTE CARLO TECHNIQUES FOR DETECTION AND RECONSTRUCTION OF

The paper describes an approach for the automatical reconstruction of homogeneous straight stairs from point cloud data by using a generative model and Markov Chain Monte Carlo techniques for estimating the parameters. Parameters for a generative model for stairs are presented. The six parameters of this 2D model are determined with a maximum-a-posteriori estimation approach. For all parameters prior probability distributions are chosen. Two types of likelihood functions are introduced. It is shown that four of the parameters under certain conditions can be determined via MCMC. Some results are presented.