Large-Scale, Metric Structure From Motion for Unordered Light Fields

Sotiris Nousias, Manolis Lourakis, Christos Bergeles; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 3292-3301

Abstract


This paper presents a large scale, metric Structure from Motion (SfM) pipeline for generalised cameras with overlapping fields-of-view, and demonstrates it using Light Field (LF) images. We build on recent developments in algorithms for absolute and relative pose recovery for generalised cameras and couple them with multi-view triangulation in a robust framework that advances the state-of-the-art on 3D reconstruction from LFs in several ways. First, our framework can recover the scale of a scene. Second, it is concerned with unordered sets of LF images, meticulously determining the order in which images should be considered. Third, it can scale to datasets with hundreds of LF images. Finally, it recovers 3D scene structure while abstaining from triangulating using very small baselines. Our approach outperforms the state-of-the-art, as demonstrated by real-world experiments with variable size datasets.

Related Material


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[bibtex]
@InProceedings{Nousias_2019_CVPR,
author = {Nousias, Sotiris and Lourakis, Manolis and Bergeles, Christos},
title = {Large-Scale, Metric Structure From Motion for Unordered Light Fields},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}