Geometry Guided Three-Dimensional Propagation for Depth From Small Motion

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In this letter, we present an accurate Depth from Small Motion approach, which reconstructs three-dimensional (3-D) depth from image sequences with extremely narrow baselines. We start with estimating sparse 3-D points and camera poses via the structure from motion method. For dense depth reconstruction, we propose a novel depth propagation using a geometric guidance term that considers not only the geometric constraint from the surface normal, but also color consistency. In addition, we propose an accurate surface normal estimation method with a multiple range search so that the normal vector can guide the direction of the depth propagation precisely. The major benefit of our depth propagation method is that it obtains detailed structures of a scene without fronto-parallel bias. We validate our method using various indoor and outdoor datasets, and both qualitative and quantitative experimental results show that our new algorithm consistently generates better 3-D depth information than the results of existing state-of-the-art methods.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2017-12
Language
English
Article Type
Article
Keywords

ALGORITHMS

Citation

IEEE SIGNAL PROCESSING LETTERS, v.24, no.12, pp.1857 - 1861

ISSN
1070-9908
DOI
10.1109/LSP.2017.2761557
URI
http://hdl.handle.net/10203/227179
Appears in Collection
EE-Journal Papers(저널논문)
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