Robust and Globally Optimal Manhattan Frame Estimation in Near Real Time
SCIE
SCOPUS
- Title
- Robust and Globally Optimal Manhattan Frame Estimation in Near Real Time
- Authors
- Kyungdon Joo; Tae-Hyun Oh; Junsik Kim; In So Kweon
- Date Issued
- 2019-03
- Publisher
- Institute of Electrical and Electronics Engineers
- Abstract
- 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, in 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).
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/103519
- DOI
- 10.1109/TPAMI.2018.2799944
- ISSN
- 0162-8828
- Article Type
- Article
- Citation
- IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 3, page. 682 - 696, 2019-03
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