The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

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.

Keywords

3D modeling, automatic modeling, modeling from reality, 3D sensors, multi-view surface matching

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Associated Lab(s) / Group(s): 3D Computer Vision Group

Associated Project(s): Automatic 3D Modeling from Range Images

Number of pages: 8

Text Reference

Daniel Huber and Martial Hebert, "3D Modeling Using a Statistical Sensor Model and Stochastic Search," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June, 2003, pp. 858-865.

BibTeX Reference

@inproceedings{Huber_2003_4427,
   author = "Daniel Huber and Martial Hebert",
   title = "3D Modeling Using a Statistical Sensor Model and Stochastic Search",
   booktitle = "Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
   pages = "858-865",
   month = "June",
   year = "2003",
}

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