TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
EpiLoc: Deep Camera Localization Under Epipolar Constraint |
| 영문제목(English Title) |
EpiLoc: Deep Camera Localization Under Epipolar Constraint |
| 저자(Author) |
Luoyuan Xu
Tao Guan
Yawei Luo
Yuesong Wang
Zhuo Chen
WenKai Liu
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| 원문수록처(Citation) |
VOL 16 NO. 06 PP. 2044 ~ 2059 (2022. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Recent works have shown that the geometric constraint can be harnessed to boost the performance of CNN-based camera localization. However, the existing strategies are limited to imposing image-level constraint between pose pairs, which is weak and coarse-gained. In this paper, we introduce a pixel-level epipolar geometry constraint to vanilla localization framework without the ground-truth 3D information. Dubbed EpiLoc, our method establishes the geometric relationship between pixels in different images by utilizing the epipolar geometry thus forcing the network to regress more accurate poses. We also propose a variant called EpiSingle to cope with non-sequential training images, which can construct the epipolar geometry constraint based on a single image in a self-supervised manner. Extensive experiments on the public indoor 7Scenes and outdoor RobotCar datasets show that the proposed pixel-level constraint is valuable, and helps our EpiLoc achieve state-of-the-art results in the end-to-end camera localization task.
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| 키워드(Keyword) |
Camera localization
End-to-end
Epipolar geometry
Pixel-level constraint
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| 파일첨부 |
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