Manhattan-world stereo

Multi-view stereo (MVS) algorithms now produce reconstructions that rival laser range scanner accuracy. However, stereo algorithms require textured surfaces, and therefore work poorly for many architectural scenes (e.g., building interiors with textureless, painted walls). This paper presents a nove...

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Bibliographic Details
Published in2009 IEEE Conference on Computer Vision and Pattern Recognition pp. 1422 - 1429
Main Authors Furukawa, Yasutaka, Curless, Brian, Seitz, Steven M, Szeliski, Richard
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2009
Subjects
Buildings
Cities and towns
Geometry
Image reconstruction
Layout
Markov random fields
Stereo image processing
Stereo vision
Surface reconstruction
Surface texture
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Summary:Multi-view stereo (MVS) algorithms now produce reconstructions that rival laser range scanner accuracy. However, stereo algorithms require textured surfaces, and therefore work poorly for many architectural scenes (e.g., building interiors with textureless, painted walls). This paper presents a novel MVS approach to overcome these limitations for Manhattan World scenes, i.e., scenes that consists of piece-wise planar surfaces with dominant directions. Given a set of calibrated photographs, we first reconstruct textured regions using an existing MVS algorithm, then extract dominant plane directions, generate plane hypotheses, and recover per-view depth maps using Markov random fields. We have tested our algorithm on several datasets ranging from office interiors to outdoor buildings, and demonstrate results that outperform the current state of the art for such texture-poor scenes.
ISBN:1424439922
9781424439928
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2009.5206867