Continuous Global Optimization in Multiview 3D Reconstruction.
Kleinberg, Jon M.
Mitchell, John C.
Pandu Rangan, C.
Vardi, Moshe Y.
Yuille, Alan L.
Energy Minimization Methods in Computer Vision & Pattern Recognition (9783540741954); 2007, p441-452, 12p
In this work, we introduce a robust energy model for multiview 3D reconstruction that fuses silhouette- and stereo-based image information. It allows to cope with significant amounts of noise without manual pre-segmentation of the input images. Moreover, we suggest a method that can globally optimize this energy up to the visibility constraint. While similar global optimization has been presented in the discrete context in form of the maxflow-mincut framework, we suggest the use of a continuous counterpart. In contrast to graph cut methods, discretizations of the continuous optimization technique are consistent and independent of the choice of the grid connectivity. Our experiments demonstrate that this leads to visible improvements. Moreover, memory requirements are reduced, allowing for global reconstructions at higher resolutions. [ABSTRACT FROM AUTHOR]
Copyright of Energy Minimization Methods in Computer Vision & Pattern Recognition (9783540741954) is the property of Springer eBooks and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)