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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/2512.22304 |
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| _version_ | 1866909976706940928 |
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| author | Bright, Darrin Raj, Rakshith Keisham, Kanchan |
| author_facet | Bright, Darrin Raj, Rakshith Keisham, Kanchan |
| contents | Accurate food nutrition estimation from single images is challenging due to the loss of 3D information. While depth-based methods provide reliable geometry, they remain inaccessible on most smartphones because of depth-sensor requirements. To overcome this challenge, we propose PortionNet, a novel cross-modal knowledge distillation framework that learns geometric features from point clouds during training while requiring only RGB images at inference. Our approach employs a dual-mode training strategy where a lightweight adapter network mimics point cloud representations, enabling pseudo-3D reasoning without any specialized hardware requirements. PortionNet achieves state-of-the-art performance on MetaFood3D, outperforming all previous methods in both volume and energy estimation. Cross-dataset evaluation on SimpleFood45 further demonstrates strong generalization in energy estimation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_22304 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PortionNet: Distilling 3D Geometric Knowledge for Food Nutrition Estimation Bright, Darrin Raj, Rakshith Keisham, Kanchan Computer Vision and Pattern Recognition Accurate food nutrition estimation from single images is challenging due to the loss of 3D information. While depth-based methods provide reliable geometry, they remain inaccessible on most smartphones because of depth-sensor requirements. To overcome this challenge, we propose PortionNet, a novel cross-modal knowledge distillation framework that learns geometric features from point clouds during training while requiring only RGB images at inference. Our approach employs a dual-mode training strategy where a lightweight adapter network mimics point cloud representations, enabling pseudo-3D reasoning without any specialized hardware requirements. PortionNet achieves state-of-the-art performance on MetaFood3D, outperforming all previous methods in both volume and energy estimation. Cross-dataset evaluation on SimpleFood45 further demonstrates strong generalization in energy estimation. |
| title | PortionNet: Distilling 3D Geometric Knowledge for Food Nutrition Estimation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.22304 |