Reliable and Effective Stereo Matching for Underwater Scenes
Stereo matching plays a vital role in underwater environments, where accurate depth estimation is crucial for applications such as robotics and marine exploration. However, underwater imaging presents significant challenges, including noise, blurriness, and optical distortions that hinder effective...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/23/4570 |
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| _version_ | 1849220438420357120 |
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| author | Lvwei Zhu Ying Gao Jiankai Zhang Yongqing Li Xueying Li |
| author_facet | Lvwei Zhu Ying Gao Jiankai Zhang Yongqing Li Xueying Li |
| author_sort | Lvwei Zhu |
| collection | DOAJ |
| description | Stereo matching plays a vital role in underwater environments, where accurate depth estimation is crucial for applications such as robotics and marine exploration. However, underwater imaging presents significant challenges, including noise, blurriness, and optical distortions that hinder effective stereo matching. This study develops two specialized stereo matching networks: UWNet and its lightweight counterpart, Fast-UWNet. UWNet utilizes self- and cross-attention mechanisms alongside an adaptive 1D-2D cross-search to enhance cost volume representation and refine disparity estimation through a cascaded update module, effectively addressing underwater imaging challenges. Due to the need for timely responses in underwater operations by robots and other devices, real-time processing speed is critical for task completion. Fast-UWNet addresses this challenge by prioritizing efficiency, eliminating the reliance on the time-consuming recurrent updates commonly used in traditional methods. Instead, it directly converts the cost volume into a set of disparity candidates and their associated confidence scores. Adaptive interpolation, guided by content and confidence information, refines the cost volume to produce the final accurate disparity. This streamlined approach achieves an impressive inference speed of 0.02 s per image. Comprehensive tests conducted in diverse underwater settings demonstrate the effectiveness of both networks, showcasing their ability to achieve reliable depth perception. |
| format | Article |
| id | doaj-art-11e6abed16ea4a688328af0883165681 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-11e6abed16ea4a688328af08831656812024-12-13T16:31:18ZengMDPI AGRemote Sensing2072-42922024-12-011623457010.3390/rs16234570Reliable and Effective Stereo Matching for Underwater ScenesLvwei Zhu0Ying Gao1Jiankai Zhang2Yongqing Li3Xueying Li4School of Data Science, Qingdao University of Science and Technology, Qingdao 266061, ChinaSchool of Data Science, Qingdao University of Science and Technology, Qingdao 266061, ChinaSchool of Data Science, Qingdao University of Science and Technology, Qingdao 266061, ChinaSchool of Data Science, Qingdao University of Science and Technology, Qingdao 266061, ChinaSchool of Data Science, Qingdao University of Science and Technology, Qingdao 266061, ChinaStereo matching plays a vital role in underwater environments, where accurate depth estimation is crucial for applications such as robotics and marine exploration. However, underwater imaging presents significant challenges, including noise, blurriness, and optical distortions that hinder effective stereo matching. This study develops two specialized stereo matching networks: UWNet and its lightweight counterpart, Fast-UWNet. UWNet utilizes self- and cross-attention mechanisms alongside an adaptive 1D-2D cross-search to enhance cost volume representation and refine disparity estimation through a cascaded update module, effectively addressing underwater imaging challenges. Due to the need for timely responses in underwater operations by robots and other devices, real-time processing speed is critical for task completion. Fast-UWNet addresses this challenge by prioritizing efficiency, eliminating the reliance on the time-consuming recurrent updates commonly used in traditional methods. Instead, it directly converts the cost volume into a set of disparity candidates and their associated confidence scores. Adaptive interpolation, guided by content and confidence information, refines the cost volume to produce the final accurate disparity. This streamlined approach achieves an impressive inference speed of 0.02 s per image. Comprehensive tests conducted in diverse underwater settings demonstrate the effectiveness of both networks, showcasing their ability to achieve reliable depth perception.https://www.mdpi.com/2072-4292/16/23/4570underwater imagingstereo matchingdepth estimation |
| spellingShingle | Lvwei Zhu Ying Gao Jiankai Zhang Yongqing Li Xueying Li Reliable and Effective Stereo Matching for Underwater Scenes Remote Sensing underwater imaging stereo matching depth estimation |
| title | Reliable and Effective Stereo Matching for Underwater Scenes |
| title_full | Reliable and Effective Stereo Matching for Underwater Scenes |
| title_fullStr | Reliable and Effective Stereo Matching for Underwater Scenes |
| title_full_unstemmed | Reliable and Effective Stereo Matching for Underwater Scenes |
| title_short | Reliable and Effective Stereo Matching for Underwater Scenes |
| title_sort | reliable and effective stereo matching for underwater scenes |
| topic | underwater imaging stereo matching depth estimation |
| url | https://www.mdpi.com/2072-4292/16/23/4570 |
| work_keys_str_mv | AT lvweizhu reliableandeffectivestereomatchingforunderwaterscenes AT yinggao reliableandeffectivestereomatchingforunderwaterscenes AT jiankaizhang reliableandeffectivestereomatchingforunderwaterscenes AT yongqingli reliableandeffectivestereomatchingforunderwaterscenes AT xueyingli reliableandeffectivestereomatchingforunderwaterscenes |