SOLD2 : Self-supervised occlusion-aware line description and detection
(2021) 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021 p.11363-11373- Abstract
- Compared to feature point detection and description, detecting and matching line segments offer additional challenges. Yet, line features represent a promising complement to points for multi-view tasks. Lines are indeed well-defined by the image gradient, frequently appear even in poorly textured areas and offer robust structural cues. We thus hereby introduce the first joint detection and description of line segments in a single deep network. Thanks to a self-supervised training, our method does not require any annotated line labels and can therefore generalize to any dataset. Our detector offers repeatable and accurate localization of line segments in images, departing from the wireframe parsing approach. Leveraging the recent progresses... (More)
- Compared to feature point detection and description, detecting and matching line segments offer additional challenges. Yet, line features represent a promising complement to points for multi-view tasks. Lines are indeed well-defined by the image gradient, frequently appear even in poorly textured areas and offer robust structural cues. We thus hereby introduce the first joint detection and description of line segments in a single deep network. Thanks to a self-supervised training, our method does not require any annotated line labels and can therefore generalize to any dataset. Our detector offers repeatable and accurate localization of line segments in images, departing from the wireframe parsing approach. Leveraging the recent progresses in descriptor learning, our proposed line descriptor is highly discriminative, while remaining robust to viewpoint changes and occlusions. We evaluate our approach against previous line detection and description methods on several multi-view datasets created with homographic warps as well as real-world viewpoint changes. Our full pipeline yields higher repeatability, localization accuracy and matching metrics, and thus represents a first step to bridge the gap with learned feature points methods. Code and trained weights are available at https://github.com/cvg/SOLD2. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/0eb2dd71-a23b-4e00-9898-0a9f9960a8e4
- author
- Pautrat, Rémi ; Lin, Juan-Ting ; Larsson, Viktor LU ; Oswald, Martin R and Pollefeys, Marc
- publishing date
- 2021
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- pages
- 11 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021
- conference location
- Virtual, Online, United States
- conference dates
- 2021-06-19 - 2021-06-25
- external identifiers
-
- scopus:85115732155
- DOI
- 10.1109/CVPR46437.2021.01121
- language
- English
- LU publication?
- no
- id
- 0eb2dd71-a23b-4e00-9898-0a9f9960a8e4
- date added to LUP
- 2022-09-06 13:20:21
- date last changed
- 2024-05-18 04:04:17
@inproceedings{0eb2dd71-a23b-4e00-9898-0a9f9960a8e4, abstract = {{Compared to feature point detection and description, detecting and matching line segments offer additional challenges. Yet, line features represent a promising complement to points for multi-view tasks. Lines are indeed well-defined by the image gradient, frequently appear even in poorly textured areas and offer robust structural cues. We thus hereby introduce the first joint detection and description of line segments in a single deep network. Thanks to a self-supervised training, our method does not require any annotated line labels and can therefore generalize to any dataset. Our detector offers repeatable and accurate localization of line segments in images, departing from the wireframe parsing approach. Leveraging the recent progresses in descriptor learning, our proposed line descriptor is highly discriminative, while remaining robust to viewpoint changes and occlusions. We evaluate our approach against previous line detection and description methods on several multi-view datasets created with homographic warps as well as real-world viewpoint changes. Our full pipeline yields higher repeatability, localization accuracy and matching metrics, and thus represents a first step to bridge the gap with learned feature points methods. Code and trained weights are available at https://github.com/cvg/SOLD2.}}, author = {{Pautrat, Rémi and Lin, Juan-Ting and Larsson, Viktor and Oswald, Martin R and Pollefeys, Marc}}, booktitle = {{2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}}, language = {{eng}}, pages = {{11363--11373}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{SOLD2 : Self-supervised occlusion-aware line description and detection}}, url = {{http://dx.doi.org/10.1109/CVPR46437.2021.01121}}, doi = {{10.1109/CVPR46437.2021.01121}}, year = {{2021}}, }