dc.citation.conferencePlace |
US |
- |
dc.citation.conferencePlace |
Las Vegas, NV, USA |
- |
dc.citation.endPage |
1771 |
- |
dc.citation.startPage |
1763 |
- |
dc.citation.title |
IEEE Conference on Computer Vision and Pattern Recognition |
- |
dc.contributor.author |
Joo, Kyungdon |
- |
dc.contributor.author |
Oh, Tae-Hyun |
- |
dc.contributor.author |
Kim, Junsik |
- |
dc.contributor.author |
Kweon, In So |
- |
dc.date.accessioned |
2023-12-19T20:36:56Z |
- |
dc.date.available |
2023-12-19T20:36:56Z |
- |
dc.date.created |
2020-11-04 |
- |
dc.date.issued |
2016-06-28 |
- |
dc.description.abstract |
Given a set of surface normals, we pose a Manhattan Frame (MF) estimation problem as a consensus set maximization that maximizes the number of inliers over the rotation search space. We solve this problem through a branchand-bound framework, which mathematically guarantees a globally optimal solution. However, the computational time of conventional branch-and-bound algorithms are intractable for real-time performance. In this paper, we propose a novel bound computation method within an efficient measurement domain for MF estimation, i.e., the extended Gaussian image (EGI). By relaxing the original problem, we can compute the bounds in real-time, while preserving global optimality. Furthermore, we quantitatively and qualitatively demonstrate the performance of the proposed method for synthetic and real-world data. We also show the versatility of our approach through two applications: extension to multiple MF estimation and video stabilization. © 2016 IEEE. |
- |
dc.identifier.bibliographicCitation |
IEEE Conference on Computer Vision and Pattern Recognition, pp.1763 - 1771 |
- |
dc.identifier.doi |
10.1109/CVPR.2016.195 |
- |
dc.identifier.issn |
1063-6919 |
- |
dc.identifier.scopusid |
2-s2.0-84986296784 |
- |
dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/66486 |
- |
dc.language |
영어 |
- |
dc.publisher |
IEEE Computer Society |
- |
dc.title |
Globally Optimal Manhattan Frame Estimation in Real-Time |
- |
dc.type |
Conference Paper |
- |
dc.date.conferenceDate |
2016-06-26 |
- |