PointNet++ [22] is a deep network based on PointNet. Although it enhances the ability to extract complex features by fusing features extracted from different receptive fields, its ability to perceive complex stand structures still needs to be improved. [PERSON] et al. [23] proposed VoteNet, which can offset each point to the corresponding instance center to increase the discrimination of different objects. However, the center offset loss function does not take into account the different contributions of each point to the loss function, resulting in an unsatisfactory offset result. PointGroup [24] segments each object through the clustering algorithm after the center offset but ignores the over-segmentation problem caused by the deep network randomness. Therefore, the segmentation accuracy still needs to be improved. [PERSON] et al. [25] proposed HAIS, which combinesPointGroup with a set aggregation algorithm to reduce the over-segmentation problem. However, the HAIS set aggregation algorithm aggregates the point sets which are close to each other, which causes the point set of a tree to be aggregated into another adjacent tree, causing over-segmentation. [PERSON] et al. [26] proposed Transformer, which has a strong perception ability for complex structures. On this basis, [PERSON] [27] proposed the Point Transformer, which performs point cloud segmentation for indoor objects. However, how to segment forest point clouds with complex structures remains to be solved. In general, although deep learning methods based on point clouds have become more and more mature, there are still many problems to be solved when applying them to segment individual trees [19]. Inspired by VoteNet and HAIS, if the relatively scattered data points are offset to the center of each tree to enhance the discrimination among different trees, then the ITS can be realized by applying the clustering algorithm. However, airborne laser lidar has poor penetration and cannot scan complete individual trees (especially in locations with severe canopy occlusion) [8]. At this time, the calculated individual tree center point has a huge gap with the actual individual tree center point, which seriously interferes with the learning effect of the deep network, resulting in a poor ability to enhance the discrimination among trees. Considering the treetop points are distinct and stable, an extreme offset loss function for ITS is designed based on HAIS, which makes the points offset to their respective treetops to enhance the discrimination among different trees. Then, the density of the treetop increases significantly after points are offset to their treetops. On this basis, the mean shift algorithm is adopted to segment the individual trees preliminarily. Due to the deep network randomness, the offset effect of some points is poor. Directly using the mean shift algorithm for clustering may suffer from serious under-segmentation and over-segmentation problems. Considering the treetop density increases significantly after extreme offset, the average proximity distance is adaptively generated as the bandwidth of the mean shift algorithm so that the dense points near the treetop are clustered into one class. At the same time, the isolated points with poor offset are clustered separately to eliminate the under-segmentation error as much as possible. Finally, in order to reduce the over-segmentation error, the ITS set aggregation based on the gradient change criterion is designed to merge the sets with close neighbors and gentle gradient change. The remainder of the paper is organized as follows. Section 2 describes the key steps of the data preprocessing and methodology. Section 3 describes the experimental data and evaluation metrics. Section 4 carries out a validity experiment, ablation experiment, and contrast experiment and analyzes the results. The discussion of the proposed method is given in Section 5. Finally, concluding remarks are given in Section 6. In general, the key contributions of our work are as follows. * Extreme offset deep learning method.