Probabilistic Labeling Cost for High-Accuracy Multi-View Reconstruction

Ilya Kostrikov, Esther Horbert, Bastian Leibe; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 1534-1541

Abstract


In this paper, we propose a novel labeling cost for multi- view reconstruction. Existing approaches use data terms with specific weaknesses that are vulnerable to common challenges, such as low-textured regions or specularities. Our new probabilistic method implicitly discards outliers and can be shown to become more exact the closer we get to the true object surface. Our approach achieves top results among all published methods on the Middlebury DINO SPARSE dataset and also delivers accurate results on several other datasets with widely varying challenges, for which it works in unchanged form.

Related Material


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[bibtex]
@InProceedings{Kostrikov_2014_CVPR,
author = {Kostrikov, Ilya and Horbert, Esther and Leibe, Bastian},
title = {Probabilistic Labeling Cost for High-Accuracy Multi-View Reconstruction},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2014}
}