Robust and Globally Optimal Manhattan Frame Estimation in Near Real Time

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Most man-made environments, such as urban and indoor scenes, consist of a set of parallel and orthogonal planar structures. These structures are approximated by the Manhattan world assumption, of which notion can be represented as a Manhattan Frame (MF). Given a set of inputs such as surface normals or vanishing points, we pose an MF estimation problem as a consensus set maximization that maximizes the number of inliers over the rotation search space. Conventionally this problem can be solved by a branch-and-bound framework which mathematically guarantees global optimality. However, the computational time of the conventional branch-and-bound algorithms is rather far from real-time. In this paper, we propose a novel bound computation method on an efficient measurement domain for MF estimation, i.e., the extended Gaussian image (EGI). By relaxing the original problem, we can compute the bound with a constant complexity, while preserving global optimality. Furthermore, we quantitatively and qualitatively demonstrate the performance of the proposed method for various synthetic and real-world data. We also show the versatility of our approach through three different applications: extension to multiple MF estimation, 3D rotation based video stabilization and vanishing point estimation (line clustering).
Publisher
IEEE COMPUTER SOC
Issue Date
2019-03
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, v.41, no.3, pp.682 - 696

ISSN
0162-8828
DOI
10.1109/TPAMI.2018.2799944
URI
http://hdl.handle.net/10203/251481
Appears in Collection
EE-Journal Papers(저널논문)
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