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 o...

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Veröffentlicht in:2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) S. 539 - 543
Hauptverfasser: Lu, Yawen, Huang, Yunhan, Sun, Su, Zhang, Tansi, Zhang, Xuewen, Fei, Songlin, Chen, Victor
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 16.03.2024
Schlagworte:
Benchmark testing
Computer vision
Computing methodologies-Artificial intelligence-Computer vision-Image segmentation / Object detection
Computing methodologies-Modeling and simulation-Simulation support systems-Simulation environments
Forestry
Three-dimensional displays
Training
Vegetation
Virtual reality
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Zusammenfassung: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 a 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.
DOI:10.1109/VRW62533.2024.00104