Graphics enhanced version of this site

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

Jump to: Download | Abstract | Notes | Text Reference | BibTeX Reference

Adobe portable document format (pdf) [319 KB]

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

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.

Associated center: VASC
Associated lab/group: 3D Computer Vision Group
Associated project: Automatic 3D Modeling from Range Images

Number of pages: 8

D. Huber and M. 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.

@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)",
   month = "June",
   year = "2003",
   pages = "858-865"
}