3D Modeling Using a Statistical Sensor Model and Stochastic Search - Robotics Institute Carnegie Mellon University

3D Modeling Using a Statistical Sensor Model and Stochastic Search

Conference Paper, Proceedings of (CVPR) Computer Vision and Pattern Recognition, pp. 858 - 865, June, 2003

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.

BibTeX

@conference{Huber-2003-8669,
author = {Daniel Huber and Martial Hebert},
title = {3D Modeling Using a Statistical Sensor Model and Stochastic Search},
booktitle = {Proceedings of (CVPR) Computer Vision and Pattern Recognition},
year = {2003},
month = {June},
pages = {858 - 865},
keywords = {3D modeling, automatic modeling, modeling from reality, 3D sensors, multi-view surface matching},
}