The ability to predict future structure features of environments based on
past perception information is extremely needed by autonomous vehicles, which
helps to make the following decision-making and path planning more reasonable.
Recently, point cloud prediction (PCP) is utilized to predict and describe
future environmental structures by the point cloud form. In this letter, we
propose a novel efficient Transformer-based network to predict the future LiDAR
point clouds exploiting the past point cloud sequences. We also design a
semantic auxiliary training strategy to make the predicted LiDAR point cloud
sequence semantically similar to the ground truth and thus improves the
significance of the deployment for more tasks in real-vehicle applications. Our
approach is completely self-supervised, which means it does not require any
manual labeling and has a solid generalization ability toward different
environments. The experimental results show that our method outperforms the
state-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-trained
models are available at this https URL

本文提出一种新的高效 Transformer-based 的点云预测网络，并设计了语义辅助训练策略来对未来的 LiDAR 点云进行预测，该方法是完全自监督的，并且在预测结果和语义相似性上表现出超越最先进的 PCP 方法的性能，具有良好的实时性能。