Triplane generator-based NeRF-GAN framework for single-view ship reconstruction
Acquiring sufficient visual information for the three-dimensional (3D) reconstruction of ships in navigation is particularly challenging. With the evolution of 3D reconstruction methodologies predicated on neural rendering, the computational pipeline for 3D reconstruction has undergone enhancements...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2496406 |
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| author | Tao Liu Shiqi Geng Yucheng Fu Zhengling Lei Yuchi Huo Xiaocai Zhang Fang Wang Bing Han Mei Sha Zhongdai Wu |
| author_facet | Tao Liu Shiqi Geng Yucheng Fu Zhengling Lei Yuchi Huo Xiaocai Zhang Fang Wang Bing Han Mei Sha Zhongdai Wu |
| author_sort | Tao Liu |
| collection | DOAJ |
| description | Acquiring sufficient visual information for the three-dimensional (3D) reconstruction of ships in navigation is particularly challenging. With the evolution of 3D reconstruction methodologies predicated on neural rendering, the computational pipeline for 3D reconstruction has undergone enhancements and optimizations. However, this pipeline necessitates a substantial corpus of input images. Research into 3D reconstruction from monocular images is in its nascent stages, and to date, no unsupervised deep learning approach for 3D reconstruction of ships from single-view UAV imagery exists within the realm of navigation. This paper introduces a novel network architecture for reconstructing 3D representations of ships from single-view UAV images. Initially, a priori statistical analysis of the dataset is conducted to harness color distribution information for noise generation. Subsequently, a novel generator and mask module are engineered to produce optimized feature outputs. Plus, discriminator and encoder networks, coupled with a tailored loss function, are formulated to direct model optimization. Ultimately, to demonstrate the effectiveness of our proposed method for single-view 3D reconstruction, we conducted experiments across three distinct datasets from various domains. Our method's FID value of 10.61 is impressive. At the same time, it yields an LPIPS value of 0.091, which is the best among the six different methods. |
| format | Article |
| id | doaj-art-01a6f9eab800419182a7951631653ff9 |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-01a6f9eab800419182a7951631653ff92025-08-25T11:28:16ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2496406Triplane generator-based NeRF-GAN framework for single-view ship reconstructionTao Liu0Shiqi Geng1Yucheng Fu2Zhengling Lei3Yuchi Huo4Xiaocai Zhang5Fang Wang6Bing Han7Mei Sha8Zhongdai Wu9College of Transport & Communications, Shanghai Maritime University, Shanghai, People’s Republic of ChinaCollege of Transport & Communications, Shanghai Maritime University, Shanghai, People’s Republic of ChinaCollege of Transport & Communications, Shanghai Maritime University, Shanghai, People’s Republic of ChinaCollege of Engineering Science and Technology, Shanghai Ocean University, Shanghai, People’s Republic of ChinaState Key Lab of CAD&CG, Zhejiang University, Hangzhou, People’s Republic of ChinaFaculty of Engineering and Information Technology, University of Melbourne, Parkville, AustraliaCollege of Engineering Science and Technology, Shanghai Ocean University, Shanghai, People’s Republic of ChinaState Key Laboratory of Maritime Technology and Safety, Shanghai Ship and Shipping Research Institute Co., Ltd., Shanghai, People’s Republic of ChinaCollege of Transport & Communications, Shanghai Maritime University, Shanghai, People’s Republic of ChinaState Key Laboratory of Maritime Technology and Safety, Shanghai Ship and Shipping Research Institute Co., Ltd., Shanghai, People’s Republic of ChinaAcquiring sufficient visual information for the three-dimensional (3D) reconstruction of ships in navigation is particularly challenging. With the evolution of 3D reconstruction methodologies predicated on neural rendering, the computational pipeline for 3D reconstruction has undergone enhancements and optimizations. However, this pipeline necessitates a substantial corpus of input images. Research into 3D reconstruction from monocular images is in its nascent stages, and to date, no unsupervised deep learning approach for 3D reconstruction of ships from single-view UAV imagery exists within the realm of navigation. This paper introduces a novel network architecture for reconstructing 3D representations of ships from single-view UAV images. Initially, a priori statistical analysis of the dataset is conducted to harness color distribution information for noise generation. Subsequently, a novel generator and mask module are engineered to produce optimized feature outputs. Plus, discriminator and encoder networks, coupled with a tailored loss function, are formulated to direct model optimization. Ultimately, to demonstrate the effectiveness of our proposed method for single-view 3D reconstruction, we conducted experiments across three distinct datasets from various domains. Our method's FID value of 10.61 is impressive. At the same time, it yields an LPIPS value of 0.091, which is the best among the six different methods.https://www.tandfonline.com/doi/10.1080/17538947.2025.24964063D Environment perceptionsingle view ship reconstructionmaritime digital twinneural radiance fieldgenerative adversarial network |
| spellingShingle | Tao Liu Shiqi Geng Yucheng Fu Zhengling Lei Yuchi Huo Xiaocai Zhang Fang Wang Bing Han Mei Sha Zhongdai Wu Triplane generator-based NeRF-GAN framework for single-view ship reconstruction International Journal of Digital Earth 3D Environment perception single view ship reconstruction maritime digital twin neural radiance field generative adversarial network |
| title | Triplane generator-based NeRF-GAN framework for single-view ship reconstruction |
| title_full | Triplane generator-based NeRF-GAN framework for single-view ship reconstruction |
| title_fullStr | Triplane generator-based NeRF-GAN framework for single-view ship reconstruction |
| title_full_unstemmed | Triplane generator-based NeRF-GAN framework for single-view ship reconstruction |
| title_short | Triplane generator-based NeRF-GAN framework for single-view ship reconstruction |
| title_sort | triplane generator based nerf gan framework for single view ship reconstruction |
| topic | 3D Environment perception single view ship reconstruction maritime digital twin neural radiance field generative adversarial network |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2496406 |
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