大规模差异化点云数据下的联邦语义分割算法

海量点云数据的存储对自动驾驶实时3D协同感知具有重要意义，然而出于数据安全保密性的要求，部分数据拥有者不愿共享其私人的点云数据，限制了模型训练准确性的提升。联邦学习是一种注重数据隐私安全的计算范式，提出了一种基于联邦学习的方法来解决车辆协同感知场景下的大规模点云语义分割问题。融合具有点间角度信息的位置编码方式并对邻近点进行几何衍射处理以增强模型的特征提取能力，最后根据本地模型的生成质量动态调整全局模型的聚合权重，提高数据局部几何结构的保持能力。在SemanticKITTI,SemanticPOSS和Toronto3D三个数据集上进行了实验，结果表明该算法显著优于单一训练数据和基于FedAvg的方法，在充分挖掘点云数据价值的同时兼顾各方数据的隐私敏感性。

Federated semantic segmentation algorithm under large scale differential point cloud data

The storage of massive point cloud data is of 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, SemanticKITTI, 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.