  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.
