Tree Completion Net: A Novel Vegetation Point Clouds Completion Model Based on Deep Learning

To improve the integrity of vegetation point clouds, the missing vegetation point can be compensated through vegetation point clouds completion technology. Further, it can enhance the accuracy of these point clouds' applications, particularly in terms of quantitative calculations, such as for the urban living vegetation volume (LVV). However, owing to factors like the mutual occlusion between ground objects, sensor perspective, and penetration ability limitations resulting in missing single tree point clouds' structures, the existing completion techniques cannot be directly applied to the single tree point clouds' completion. This study combines the cutting-edge deep learning techniques, for example, the self-supervised and multiscale Encoder (Decoder), to propose a tree completion net (TC-Net) model that is suitable for the single tree structure completion. Being motivated by the attenuation of electromagnetic waves through a uniform medium, this study proposes an uneven density loss pattern. This study uses the local similarity visualization method, which is different from ordinary Chamfer distance (CD) values and can better assist in visually assessing the effects of point cloud completion. Experimental results indicate that the TC-Net model, based on the uneven density loss pattern, effectively identifies and compensates for the missing structures of single tree point clouds in real scenarios, thus reducing the average CD value by above 2.0, with the best result dropping from 23.89 to 13.08. Meanwhile, experiments on a large-scale tree dataset show that TC-Net has the lowest average CD value of 13.28. In the urban LVV estimates, the completed point clouds have reduced the average MAE, RMSE, and MAPE from 9.57, 7.78, and 14.11% to 1.86, 2.84, and 5.23%, respectively, thus demonstrating the effectiveness of TC-Net.