Tomato-Nerf: Advancing Tomato Model Reconstruction With Improved Neural Radiance Fields
The real-time simulation of large-scale agricultural operations will offer farmers data-driven and physically consistent decision support, facilitated by predictive digital twins. To construct a predictive digital twin, the initial step involves 3D reconstruction of plant geometry. In this paper, a...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10589455/ |
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| _version_ | 1850109004450103296 |
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| author | Xiajun Zheng Xinyi AI Hao Qin Jiacheng Rong Zhiqin Zhang Yan Yang Ting Yuan Wei Li |
| author_facet | Xiajun Zheng Xinyi AI Hao Qin Jiacheng Rong Zhiqin Zhang Yan Yang Ting Yuan Wei Li |
| author_sort | Xiajun Zheng |
| collection | DOAJ |
| description | The real-time simulation of large-scale agricultural operations will offer farmers data-driven and physically consistent decision support, facilitated by predictive digital twins. To construct a predictive digital twin, the initial step involves 3D reconstruction of plant geometry. In this paper, a high-resolution, accurate 3D reconstruction of tomato plants, Tomato-NeRF, is proposed, which is specially used for three-dimensional reconstruction of tomato plants. Our approach used a modular design to integrate ideas from their research paper into Tomato-NeRF. By using hash encoding to map coordinates to trainable feature vectors, we balance quality, memory usage, and performance in NeRF training. The proposal sampler targets key regions for rendering, and customized loss functions are designed to optimize specific tasks. The effectiveness of our approach is demonstrated by the ability to generate high-resolution geometric models from phone camera data. Comparative results show that Tomato-NeRF has significant advantages over Instant-NGP and MipNeRF in the tomato plant reconstruction task. The data acquisition method is simpler and more efficient than other reconstruction methods, providing a practical solution for real-time agricultural simulations. |
| format | Article |
| id | doaj-art-a02cac5cd66e4f08b97b04b14308718f |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a02cac5cd66e4f08b97b04b14308718f2025-08-20T02:38:13ZengIEEEIEEE Access2169-35362024-01-011218420618421510.1109/ACCESS.2024.342490810589455Tomato-Nerf: Advancing Tomato Model Reconstruction With Improved Neural Radiance FieldsXiajun Zheng0https://orcid.org/0009-0009-7697-8877Xinyi AI1Hao Qin2Jiacheng Rong3Zhiqin Zhang4Yan Yang5Ting Yuan6Wei Li7College of Engineering, China Agricultural University, Beijing, ChinaCollege of Engineering, China Agricultural University, Beijing, ChinaCollege of Engineering, China Agricultural University, Beijing, ChinaCollege of Engineering, China Agricultural University, Beijing, ChinaCollege of Engineering, China Agricultural University, Beijing, ChinaCollege of Engineering, China Agricultural University, Beijing, ChinaCollege of Engineering, China Agricultural University, Beijing, ChinaCollege of Engineering, China Agricultural University, Beijing, ChinaThe real-time simulation of large-scale agricultural operations will offer farmers data-driven and physically consistent decision support, facilitated by predictive digital twins. To construct a predictive digital twin, the initial step involves 3D reconstruction of plant geometry. In this paper, a high-resolution, accurate 3D reconstruction of tomato plants, Tomato-NeRF, is proposed, which is specially used for three-dimensional reconstruction of tomato plants. Our approach used a modular design to integrate ideas from their research paper into Tomato-NeRF. By using hash encoding to map coordinates to trainable feature vectors, we balance quality, memory usage, and performance in NeRF training. The proposal sampler targets key regions for rendering, and customized loss functions are designed to optimize specific tasks. The effectiveness of our approach is demonstrated by the ability to generate high-resolution geometric models from phone camera data. Comparative results show that Tomato-NeRF has significant advantages over Instant-NGP and MipNeRF in the tomato plant reconstruction task. The data acquisition method is simpler and more efficient than other reconstruction methods, providing a practical solution for real-time agricultural simulations.https://ieeexplore.ieee.org/document/10589455/3D reconstructionagricultural automationdeep learningNeRFmulti-view imaging |
| spellingShingle | Xiajun Zheng Xinyi AI Hao Qin Jiacheng Rong Zhiqin Zhang Yan Yang Ting Yuan Wei Li Tomato-Nerf: Advancing Tomato Model Reconstruction With Improved Neural Radiance Fields IEEE Access 3D reconstruction agricultural automation deep learning NeRF multi-view imaging |
| title | Tomato-Nerf: Advancing Tomato Model Reconstruction With Improved Neural Radiance Fields |
| title_full | Tomato-Nerf: Advancing Tomato Model Reconstruction With Improved Neural Radiance Fields |
| title_fullStr | Tomato-Nerf: Advancing Tomato Model Reconstruction With Improved Neural Radiance Fields |
| title_full_unstemmed | Tomato-Nerf: Advancing Tomato Model Reconstruction With Improved Neural Radiance Fields |
| title_short | Tomato-Nerf: Advancing Tomato Model Reconstruction With Improved Neural Radiance Fields |
| title_sort | tomato nerf advancing tomato model reconstruction with improved neural radiance fields |
| topic | 3D reconstruction agricultural automation deep learning NeRF multi-view imaging |
| url | https://ieeexplore.ieee.org/document/10589455/ |
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