Due to factors such as equipment failure and environmental interference, lidar often encounters problems of missing data or noise interference during the data collection process, which seriously affect the subsequent analysis and application of the data. To solve this problem, the diffusion Transformer network (DT-Net) is introduced and used as a generator in combination with a Self-Attention unit discriminator. Additionally, a diffusion mechanism is designed for lidar data completion. The experimental results show that compared to the PoinTr method, the proposed approach achieves significant improvements in lidar data completion tasks, with an average Chamfer distance(CD) value reduced by approximately 1.79% and an F-Score value increased by approximately 1.88%.