Accurate, Dense, and Robust Multiview Stereopsis

This paper proposes a novel algorithm for multiview stereopsis that outputs a dense set of small rectangular patches covering the surfaces visible in the images. Stereopsis is implemented as a match, expand, and filter procedure, starting from a sparse set of matched keypoints, and repeatedly expand...

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Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence Jg. 32; H. 8; S. 1362 - 1376
Hauptverfasser: Furukawa, Yasutaka, Ponce, Jean
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Los Alamitos, CA IEEE 01.08.2010
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
3D/stereo scene analysis
Algorithms
Applied sciences
Artificial intelligence
Benchmark testing
Buildings
Computer science; control theory; systems
Computer vision
Consistency
Exact sciences and technology
Image motion analysis
Image reconstruction
Intelligence
Layout
Matched filters
Mathematical models
modeling and recovery of physical attributes
motion
Motion analysis
Obstacles
Pattern recognition. Digital image processing. Computational geometry
Photometry
Robustness
shape
Solid modeling
Studies
Visibility
Initialization
Computer vision
Stereo image processing
motion
shape
Outdoor installation
Crowd
Silhouette
Modeling
3D/stereo scene analysis
Outlier
Multiple view
Computational geometry
Matched filter
Scene analysis
Sparse set
Visible spectrum
Visibility
Stereopsis
Pattern analysis
Artificial intelligence
Photometry
modeling and recovery of physical attributes
ISSN:0162-8828, 1939-3539, 1939-3539
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Beschreibung
Zusammenfassung:This paper proposes a novel algorithm for multiview stereopsis that outputs a dense set of small rectangular patches covering the surfaces visible in the images. Stereopsis is implemented as a match, expand, and filter procedure, starting from a sparse set of matched keypoints, and repeatedly expanding these before using visibility constraints to filter away false matches. The keys to the performance of the proposed algorithm are effective techniques for enforcing local photometric consistency and global visibility constraints. Simple but effective methods are also proposed to turn the resulting patch model into a mesh which can be further refined by an algorithm that enforces both photometric consistency and regularization constraints. The proposed approach automatically detects and discards outliers and obstacles and does not require any initialization in the form of a visual hull, a bounding box, or valid depth ranges. We have tested our algorithm on various data sets including objects with fine surface details, deep concavities, and thin structures, outdoor scenes observed from a restricted set of viewpoints, and "crowded" scenes where moving obstacles appear in front of a static structure of interest. A quantitative evaluation on the Middlebury benchmark [1] shows that the proposed method outperforms all others submitted so far for four out of the six data sets.
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ISSN:0162-8828
1939-3539
1939-3539
DOI:10.1109/TPAMI.2009.161