Title:
Large-Scale Dense 3D Reconstruction from Stereo Imagery
Large-Scale Dense 3D Reconstruction from Stereo Imagery
Author(s)
Alcantarilla, Pablo F.
Beall, Chris
Dellaert, Frank
Beall, Chris
Dellaert, Frank
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Abstract
In this paper we propose a novel method for large-scale dense 3D reconstruction from stereo imagery. Assuming that stereo camera calibration and camera motion are known, our method is able to reconstruct accurately dense 3D models of urban environments in the form of point clouds. We take advantage of recent stereo matching techniques that are able
to build dense and accurate disparity maps from two rectified
images. Then, we fuse the information from multiple disparity
maps into a global model by using an efficient data association technique that takes into account stereo uncertainty and
performs geometric and photometric consistency validation in a multi-view setup. Finally, we use efficient voxel grid filtering techniques to deal with storage requirements in large-scale environments. In addition, our method automatically discards
possible moving obstacles in the scene. We show experimental
results on real video large-scale sequences and compare our
approach with respect to other state-of-the-art methods such as PMVS
and StereoScan.
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Date Issued
2013-11
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Text
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Proceedings