大规模差异化点云数据下的联邦语义分割算法.

The storage of massive point cloud data has great significance to the real-time 3D collaborative perception of autonomous driving. However, due to the requirements of data security and confidentiality, some data owners are unwilling to share their private point cloud data, which limits the improvement of model training accuracy. Federated learning is a computing paradigm that focuses on data privacy and security. This paper proposed a novel approach based on federated learning to address the challenge of large-scale point cloud semantic segmentation in collaborative vehicle perception scenarios. It integrated position encoding with inter-point angle information and geometric diffraction of neighboring points to enhance the feature extraction capability of the model. Finally, it dynamically adjusted the aggregation weights of the global model according to the generation quality of the local model to improve the ability to maintain the local geometric structure of the data. This paper applied the proposed method on three datasets, such as Semantic KITTI, SemanticPOSS and Toronto3D. The results show that the proposed approach significantly outperforms the single training data and the FedAvg-based method, and fully exploits the value of the point cloud data while taking into account the privacy sensitivity of each party's data. [ABSTRACT FROM AUTHOR]