M2fNet: Multi-modal Forest Monitoring Network on Large-scale Virtual Dataset
<|reference_start|>M2fNet: Multi-modal Forest Monitoring Network on Large-scale Virtual Dataset: 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.<|reference_end|>
@article{lu2024m2fnet:,
    title={M2fNet: Multi-modal Forest Monitoring Network on Large-scale Virtual
  Dataset},
    author={Yawen Lu, Yunhan Huang, Su Sun, Tansi Zhang, Xuewen Zhang, Songlin
  Fei, Yingjie Chen},
    journal={arXiv preprint arXiv:2402.04534},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.04534},
    primaryClass={cs.GR}
}