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The ability to predict future structure features of environments based onpast perception information is extremely needed by autonomous vehicles, whichhelps to make the following decision-making and path planning more reasonable.Recently, point cloud prediction (PCP) is utilized to predict and describefuture environmental structures by the point cloud form. In this letter, wepropose a novel efficient Transformer-based network to predict the future LiDARpoint clouds exploiting the past point cloud sequences. We also design asemantic auxiliary training strategy to make the predicted LiDAR point cloudsequence semantically similar to the ground truth and thus improves thesignificance of the deployment for more tasks in real-vehicle applications. Ourapproach is completely self-supervised, which means it does not require anymanual labeling and has a solid generalization ability toward differentenvironments. The experimental results show that our method outperforms thestate-of-the-art PCP methods on the prediction results and semantic similarity,and has a good real-time performance. Our open-source code and pre-trainedmodels are available at https://github.com/Blurryface0814/PCPNet.

PCPNet: An Efficient and Semantic-Enhanced Transformer Network for Point Cloud Prediction