Neural Underwater Scene Representation

Yunkai Tang; Chengxuan Zhu; Renjie Wan; Chao Xu; Boxin Shi

Neural Underwater Scene Representation

Yunkai Tang, Chengxuan Zhu, Renjie Wan^*, Chao Xu, Boxin Shi^*

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

Abstract

Among the numerous efforts towards digitally recovering the physical world, Neural Radiance Fields (NeRFs) have proved effective in most cases. However, underwater scene introduces unique challenges due to the absorbing water medium, the local change in lighting and the dynamic contents in the scene. We aim at developing a neural underwater scene representation for these challenges, modeling the complex process of attenuation, unstable in-scattering and moving objects during light transport. The proposed method can reconstruct the scenes from both established datasets and in-the-wild videos with outstanding fidelity.

Original language	English
Title of host publication	Proceedings of 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024
Publisher	IEEE
Pages	11780-11789
Number of pages	10
Publication status	Published - Jun 2024
Event	The IEEE / CVF Computer Vision and Pattern Recognition Conference, CVPR 2024 - Seattle Convention Center, Seattle, United States Duration: 17 Jun 2024 → 21 Jun 2024 https://cvpr.thecvf.com/ (conference website) https://cvpr2023.thecvf.com/virtual/2023/calendar (Link to conference schedule) https://media.eventhosts.cc/Conferences/CVPR2024/CVPR_main_conf_2024.pdf (Link to conference booklet) https://openaccess.thecvf.com/CVPR2024 (Conference proceedings)

Publication series

Name	Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Conference

Conference	The IEEE / CVF Computer Vision and Pattern Recognition Conference, CVPR 2024
Abbreviated title	CVPR 2024
Country/Territory	United States
City	Seattle
Period	17/06/24 → 21/06/24
Internet address	https://cvpr.thecvf.com/ (conference website) https://cvpr2023.thecvf.com/virtual/2023/calendar (Link to conference schedule) https://media.eventhosts.cc/Conferences/CVPR2024/CVPR_main_conf_2024.pdf (Link to conference booklet) https://openaccess.thecvf.com/CVPR2024 (Conference proceedings)

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Tang, Y., Zhu, C., Wan, R., Xu, C., & Shi, B. (2024). Neural Underwater Scene Representation. In Proceedings of 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024 (pp. 11780-11789). (Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition). IEEE. https://openaccess.thecvf.com/content/CVPR2024/html/Tang_Neural_Underwater_Scene_Representation_CVPR_2024_paper.html

@inproceedings{38a0877b6cde4cd0a357852c80cbba5f,

title = "Neural Underwater Scene Representation",

abstract = "Among the numerous efforts towards digitally recovering the physical world, Neural Radiance Fields (NeRFs) have proved effective in most cases. However, underwater scene introduces unique challenges due to the absorbing water medium, the local change in lighting and the dynamic contents in the scene. We aim at developing a neural underwater scene representation for these challenges, modeling the complex process of attenuation, unstable in-scattering and moving objects during light transport. The proposed method can reconstruct the scenes from both established datasets and in-the-wild videos with outstanding fidelity.",

author = "Yunkai Tang and Chengxuan Zhu and Renjie Wan and Chao Xu and Boxin Shi",

note = "This work is supported by National Natural Science Foundation of China under Grant No. 62136001, 62088102, 62276007. Renjie Wan is supported by the National Natural Science Foundation of China under Grant No. 62302415, Guangdong Basic and Applied Basic Research Foundation under Grant No. 2022A1515110692, 2024A1515012822, and the Blue Sky Research Fund of HKBU under Grant No. BSRF/21-22/16.; The IEEE / CVF Computer Vision and Pattern Recognition Conference, CVPR 2024, CVPR 2024 ; Conference date: 17-06-2024 Through 21-06-2024",

year = "2024",

month = jun,

language = "English",

series = "Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition",

publisher = "IEEE",

pages = "11780--11789",

booktitle = "Proceedings of 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024",

address = "United States",

url = "https://cvpr.thecvf.com/, https://cvpr2023.thecvf.com/virtual/2023/calendar, https://media.eventhosts.cc/Conferences/CVPR2024/CVPR_main_conf_2024.pdf, https://openaccess.thecvf.com/CVPR2024",

}

Tang, Y, Zhu, C, Wan, R, Xu, C & Shi, B 2024, Neural Underwater Scene Representation. in Proceedings of 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, pp. 11780-11789, The IEEE / CVF Computer Vision and Pattern Recognition Conference, CVPR 2024, Seattle, Washington, United States, 17/06/24. <https://openaccess.thecvf.com/content/CVPR2024/html/Tang_Neural_Underwater_Scene_Representation_CVPR_2024_paper.html>

Neural Underwater Scene Representation. / Tang, Yunkai; Zhu, Chengxuan; Wan, Renjie et al.
Proceedings of 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024. IEEE, 2024. p. 11780-11789 (Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Neural Underwater Scene Representation

AU - Tang, Yunkai

AU - Zhu, Chengxuan

AU - Wan, Renjie

AU - Xu, Chao

AU - Shi, Boxin

N1 - This work is supported by National Natural Science Foundation of China under Grant No. 62136001, 62088102, 62276007. Renjie Wan is supported by the National Natural Science Foundation of China under Grant No. 62302415, Guangdong Basic and Applied Basic Research Foundation under Grant No. 2022A1515110692, 2024A1515012822, and the Blue Sky Research Fund of HKBU under Grant No. BSRF/21-22/16.

PY - 2024/6

Y1 - 2024/6

N2 - Among the numerous efforts towards digitally recovering the physical world, Neural Radiance Fields (NeRFs) have proved effective in most cases. However, underwater scene introduces unique challenges due to the absorbing water medium, the local change in lighting and the dynamic contents in the scene. We aim at developing a neural underwater scene representation for these challenges, modeling the complex process of attenuation, unstable in-scattering and moving objects during light transport. The proposed method can reconstruct the scenes from both established datasets and in-the-wild videos with outstanding fidelity.

AB - Among the numerous efforts towards digitally recovering the physical world, Neural Radiance Fields (NeRFs) have proved effective in most cases. However, underwater scene introduces unique challenges due to the absorbing water medium, the local change in lighting and the dynamic contents in the scene. We aim at developing a neural underwater scene representation for these challenges, modeling the complex process of attenuation, unstable in-scattering and moving objects during light transport. The proposed method can reconstruct the scenes from both established datasets and in-the-wild videos with outstanding fidelity.

UR - https://www.youtube.com/watch?v=qaWLwv8VxlI

UR - https://cvpr2023.thecvf.com/virtual/2024/poster/29691

M3 - Conference proceeding

T3 - Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

SP - 11780

EP - 11789

BT - Proceedings of 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024

PB - IEEE

T2 - The IEEE / CVF Computer Vision and Pattern Recognition Conference, CVPR 2024

Y2 - 17 June 2024 through 21 June 2024

ER -

Neural Underwater Scene Representation

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