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3D modeling using a statistical sensor model and stochastic search

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2 Author(s)
Huber, D.F. ; Robotics Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA ; Hebert, M.

Accurate and robust registration of multiple three-dimensional (3D) views is crucial for creating digital 3D models of real-world scenes. In this paper, we present a framework for evaluating the quality of model hypotheses during the registration phase. We use maximum likelihood estimation to learn a probabilistic model of registration success. This method provides a principled way to combine multiple measures of registration accuracy. Also, we describe a stochastic algorithm for robustly searching the large space of possible models for the best model hypothesis. This new approach can detect situations in which no solution exists, outputting a set of model parts if a single model using all the views cannot be found. We show results for a large collection of automatically modeled scenes and demonstrate that our algorithm works independently of scene size and the type of range sensor. This work is part of a system we have developed to automate the 3D modeling process for a set of 3D views obtained from unknown sensor viewpoints.

Published in:

Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on  (Volume:1 )

Date of Conference:

18-20 June 2003