Date of Original Version
Abstract or Description
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. 1 Introduction Modeling-from-reality is the process of creating digital three-dimensional (3D) models of real-world scenes from 3D views as obtained, for example, from range sensors or stereo camera systems
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR),, 858-865.