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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2505.08811 |
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| _version_ | 1866912991220334592 |
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| author | Lian, Shijie Zhang, Ziyi Li, Hua Yang, Laurence Tianruo Ren, Mengyu Liu, Debin Wu, Wenhui |
| author_facet | Lian, Shijie Zhang, Ziyi Li, Hua Yang, Laurence Tianruo Ren, Mengyu Liu, Debin Wu, Wenhui |
| contents | Underwater 3D scene reconstruction is crucial for multimedia applications in adverse environments, such as underwater robotic perception and navigation. However, the complexity of interactions between light propagation, water medium, and object surfaces poses significant difficulties for existing methods in accurately simulating their interplay. Additionally, expensive training and rendering costs limit their practical application. Therefore, we propose Tensorized Underwater Gaussian Splatting (TUGS), a compact underwater 3D representation based on physical modeling of complex underwater light fields. TUGS includes a physics-based underwater Adaptive Medium Estimation (AME) module, enabling accurate simulation of both light attenuation and backscatter effects in underwater environments, and introduces Tensorized Densification Strategies (TDS) to efficiently refine the tensorized representation during optimization. TUGS is able to render high-quality underwater images with faster rendering speeds and less memory usage. Extensive experiments on real-world underwater datasets have demonstrated that TUGS can efficiently achieve superior reconstruction quality using a limited number of parameters. The code is available at https://liamlian0727.github.io/TUGS |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_08811 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TUGS: Physics-based Compact Representation of Underwater Scenes by Tensorized Gaussian Lian, Shijie Zhang, Ziyi Li, Hua Yang, Laurence Tianruo Ren, Mengyu Liu, Debin Wu, Wenhui Computer Vision and Pattern Recognition Robotics Underwater 3D scene reconstruction is crucial for multimedia applications in adverse environments, such as underwater robotic perception and navigation. However, the complexity of interactions between light propagation, water medium, and object surfaces poses significant difficulties for existing methods in accurately simulating their interplay. Additionally, expensive training and rendering costs limit their practical application. Therefore, we propose Tensorized Underwater Gaussian Splatting (TUGS), a compact underwater 3D representation based on physical modeling of complex underwater light fields. TUGS includes a physics-based underwater Adaptive Medium Estimation (AME) module, enabling accurate simulation of both light attenuation and backscatter effects in underwater environments, and introduces Tensorized Densification Strategies (TDS) to efficiently refine the tensorized representation during optimization. TUGS is able to render high-quality underwater images with faster rendering speeds and less memory usage. Extensive experiments on real-world underwater datasets have demonstrated that TUGS can efficiently achieve superior reconstruction quality using a limited number of parameters. The code is available at https://liamlian0727.github.io/TUGS |
| title | TUGS: Physics-based Compact Representation of Underwater Scenes by Tensorized Gaussian |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2505.08811 |