M2fNet: Multi-modal Forest Monitoring Network on Large-scale Virtual Dataset
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914681183010816 |
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| author | Lu, Yawen Huang, Yunhan Sun, Su Zhang, Tansi Zhang, Xuewen Fei, Songlin Chen, Yingjie |
| author_facet | Lu, Yawen Huang, Yunhan Sun, Su Zhang, Tansi Zhang, Xuewen Fei, Songlin Chen, Yingjie |
| contents | Forest monitoring and education are key to forest protection, education and management, which is an effective way to measure the progress of a country's forest and climate commitments. Due to the lack of a large-scale wild forest monitoring benchmark, the common practice is to train the model on a common outdoor benchmark (e.g., KITTI) and evaluate it on real forest datasets (e.g., CanaTree100). However, there is a large domain gap in this setting, which makes the evaluation and deployment difficult. In this paper, we propose a new photorealistic virtual forest dataset and a multimodal transformer-based algorithm for tree detection and instance segmentation. To the best of our knowledge, it is the first time that a multimodal detection and segmentation algorithm is applied to large-scale forest scenes. We believe that the proposed dataset and method will inspire the simulation, computer vision, education, and forestry communities towards a more comprehensive multi-modal understanding. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_04534 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | M2fNet: Multi-modal Forest Monitoring Network on Large-scale Virtual Dataset Lu, Yawen Huang, Yunhan Sun, Su Zhang, Tansi Zhang, Xuewen Fei, Songlin Chen, Yingjie Graphics Forest monitoring and education are key to forest protection, education and management, which is an effective way to measure the progress of a country's forest and climate commitments. Due to the lack of a large-scale wild forest monitoring benchmark, the common practice is to train the model on a common outdoor benchmark (e.g., KITTI) and evaluate it on real forest datasets (e.g., CanaTree100). However, there is a large domain gap in this setting, which makes the evaluation and deployment difficult. In this paper, we propose a new photorealistic virtual forest dataset and a multimodal transformer-based algorithm for tree detection and instance segmentation. To the best of our knowledge, it is the first time that a multimodal detection and segmentation algorithm is applied to large-scale forest scenes. We believe that the proposed dataset and method will inspire the simulation, computer vision, education, and forestry communities towards a more comprehensive multi-modal understanding. |
| title | M2fNet: Multi-modal Forest Monitoring Network on Large-scale Virtual Dataset |
| topic | Graphics |
| url | https://arxiv.org/abs/2402.04534 |