Tree Point Cloud Completion Network Based on Attention Mechanism

Due to the limitations of three-dimensional laser scanners, such as resolution and environmental occlusion, the scanned point clouds of trees are usually incomplete, especially for single view scans. In light of the complex geometric structures of trees, a tree point cloud completion network is proposed in this paper, which fully learns the potential features of missing point clouds through cross-attention and self-attention mechanisms, and predicts complete tree point clouds from sparse to refined by decoding those features. To address the problem of difficult acquisition of tree completion datasets, a tree point cloud completion dataset with ground truth from different tree type using simulated scans of the single view is constructed in this paper. Experiments on this dataset show that compared to the point cloud completion network AdaPoinTr, the proposed network in this paper reduces the average Chamfer distance by 0.62 and increases the average <italic>F</italic>-score by 0.04; Thus, the proposed network can effectively complete missing point clouds of trees of different types.