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.

翻译：森林监测与教育是森林保护、教育和管理的关键，也是衡量国家森林及气候承诺进展的有效途径。由于缺乏大规模野生森林监测基准，常见做法是在通用室外基准（如KITTI）上训练模型，并在真实森林数据集（如CanaTree100）上评估。然而，这种设置存在较大领域差异，导致评估与部署困难。本文提出一种新型逼真虚拟森林数据集及基于多模态Transformer的树木检测与实例分割算法。据我们所知，这是首次将多模态检测与分割算法应用于大规模森林场景。我们相信，所提出的数据集和方法将推动仿真、计算机视觉、教育及林业领域向更全面的多模态理解方向发展。