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Content Provider | IEEE Xplore Digital Library |
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Author | Bodis-Szomoru, A. Riemenschneider, H. Van Gool, L. |
Copyright Year | 2014 |
Description | Author affiliation: Comput. Vision Lab., ETH Zurich, Zurich, Switzerland (Bodis-Szomoru, A.; Riemenschneider, H.; Van Gool, L.) |
Abstract | State-of-the-art Multi-View Stereo (MVS) algorithms deliver dense depth maps or complex meshes with very high detail, and redundancy over regular surfaces. In turn, our interest lies in an approximate, but light-weight method that is better to consider for large-scale applications, such as urban scene reconstruction from ground-based images. We present a novel approach for producing dense reconstructions from multiple images and from the underlying sparse Structure-from-Motion (SfM) data in an efficient way. To overcome the problem of SfM sparsity and textureless areas, we assume piecewise planarity of man-made scenes and exploit both sparse visibility and a fast over-segmentation of the images. Reconstruction is formulated as an energy-driven, multi-view plane assignment problem, which we solve jointly over superpixels from all views while avoiding expensive photoconsistency computations. The resulting planar primitives -- defined by detailed superpixel boundaries -- are computed in about 10 seconds per image. |
Starting Page | 469 |
Ending Page | 476 |
File Size | 1134345 |
Page Count | 8 |
File Format | |
ISBN | 9781479951185 |
ISSN | 10636919 |
DOI | 10.1109/CVPR.2014.67 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2014-06-23 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Three-dimensional displays Image reconstruction Image color analysis Image segmentation Surface reconstruction Robustness Optimization plane fitting multi-view superpixels structure-from-motion SfM reconstruction piecewise-planar photoconsistency sparse segmentation |
Content Type | Text |
Resource Type | Article |
Subject | Computer Vision and Pattern Recognition Software |
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