Towards Dense Point Correspondence with PatchMatch in Low-Resolution Sonar Images
(2022) 2022 IEEE/OES Autonomous Underwater Vehicles Symposium, AUV 2022 In 2022 IEEE/OES Autonomous Underwater Vehicles Symposium, AUV 2022- Abstract
Robust feature correspondences between 2D sonar imagery are important for perception tasks in the underwater domain such as 3D reconstruction but involve open challenges, in particular, low-resolution as well as the fact that object appearance is view-dependent. Although sonars in the MHz range would allow for higher resolution imagery, in this paper we focus on scenarios with a lower frequency kHz sensor, in which the longer visual range is gained at the sacrifice of image resolution. To this end, we first propose to solve the correspondence task using the PatchMatch algorithm for the first time in sonar imagery, and then propose a method for feature extraction based on IC. We then compare the proposed methods against conventional... (More)
Robust feature correspondences between 2D sonar imagery are important for perception tasks in the underwater domain such as 3D reconstruction but involve open challenges, in particular, low-resolution as well as the fact that object appearance is view-dependent. Although sonars in the MHz range would allow for higher resolution imagery, in this paper we focus on scenarios with a lower frequency kHz sensor, in which the longer visual range is gained at the sacrifice of image resolution. To this end, we first propose to solve the correspondence task using the PatchMatch algorithm for the first time in sonar imagery, and then propose a method for feature extraction based on IC. We then compare the proposed methods against conventional methods from computer vision. We evaluate our method on data from a lake experiment with objects captured with an FLS sensor. Our results show that the proposed combination of IC together with PatchMatch is well-suited for point feature extraction and correspondence in sonar imagery. Further, we also evaluate the different methods for point correspondence with a 3D object reconstruction task.
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- author
- Fuchs, Louise Rixon ; Gallstrom, Andreas LU and Maki, Atsuto
- organization
- publishing date
- 2022
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- 3D reconstruction, feature correspondence, FLS, PatchMatch
- host publication
- 2022 IEEE/OES Autonomous Underwater Vehicles Symposium, AUV 2022
- series title
- 2022 IEEE/OES Autonomous Underwater Vehicles Symposium, AUV 2022
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2022 IEEE/OES Autonomous Underwater Vehicles Symposium, AUV 2022
- conference location
- Singapore, Singapore
- conference dates
- 2022-09-19 - 2022-09-21
- external identifiers
-
- scopus:85143972442
- ISBN
- 9781665416894
- DOI
- 10.1109/AUV53081.2022.9965885
- language
- English
- LU publication?
- yes
- id
- 902ecdb6-68d2-4556-b8e8-847cd2ad5bff
- date added to LUP
- 2023-01-12 11:31:28
- date last changed
- 2023-01-12 11:31:28
@inproceedings{902ecdb6-68d2-4556-b8e8-847cd2ad5bff, abstract = {{<p>Robust feature correspondences between 2D sonar imagery are important for perception tasks in the underwater domain such as 3D reconstruction but involve open challenges, in particular, low-resolution as well as the fact that object appearance is view-dependent. Although sonars in the MHz range would allow for higher resolution imagery, in this paper we focus on scenarios with a lower frequency kHz sensor, in which the longer visual range is gained at the sacrifice of image resolution. To this end, we first propose to solve the correspondence task using the PatchMatch algorithm for the first time in sonar imagery, and then propose a method for feature extraction based on IC. We then compare the proposed methods against conventional methods from computer vision. We evaluate our method on data from a lake experiment with objects captured with an FLS sensor. Our results show that the proposed combination of IC together with PatchMatch is well-suited for point feature extraction and correspondence in sonar imagery. Further, we also evaluate the different methods for point correspondence with a 3D object reconstruction task.</p>}}, author = {{Fuchs, Louise Rixon and Gallstrom, Andreas and Maki, Atsuto}}, booktitle = {{2022 IEEE/OES Autonomous Underwater Vehicles Symposium, AUV 2022}}, isbn = {{9781665416894}}, keywords = {{3D reconstruction; feature correspondence; FLS; PatchMatch}}, language = {{eng}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, series = {{2022 IEEE/OES Autonomous Underwater Vehicles Symposium, AUV 2022}}, title = {{Towards Dense Point Correspondence with PatchMatch in Low-Resolution Sonar Images}}, url = {{http://dx.doi.org/10.1109/AUV53081.2022.9965885}}, doi = {{10.1109/AUV53081.2022.9965885}}, year = {{2022}}, }