3D LIDAR point clouds are extensively utilized in various domains, and data augmentation techniques for these point clouds can enhance network model convergence during training while also reducing the requisite data volume. Notably, PolarMix represents a seminal contribution to data enhancement in the realm of 3D LIDAR point Clouds Semantic Segmentation. It markedly augments the number of instances per class through swapping and rotate-paste mechanisms. Rotate-paste involves rotating and pasting selected class instances around the Z-axis multiple times. However, when capturing real-world scenarios using LiDAR point clouds, a pronounced class imbalance is observed, wherein certain classes dominate in sample numbers, while others are sparsely represented. Regrettably, PolarMix overlooks this class imbalance, leading to unequal treatment of all classes. To rectify this, we introduce the Class-Balanced PolarMix (CB-PolarMix), which operates in a cascading manner to diversify the training distribution and further optimize data augmentation outcomes. The cornerstone of CB-PolarMix lies in its adaptive reinforcement of foreground classes based on their distribution patterns. More specifically, our approach tweaks the pasting process for each class contingent upon its historical prediction accuracy. Experimental results from the SemanticPOSS and SemanticKitti datasets, utilizing the MinkowskiNet and SPVCNN models respectively, underscore the efficacy of the proposed CB-PolarMix.

3D LiDAR point clouds, obtained through scanning by LiDAR devices, contain rich information such as 3D coordinates (X, Y, Z), color, classification values, intensity values, and time. However, the original collected 3D LiDAR point clouds often exhibit significant disparities in instance counts, which can hinder the effectiveness of point cloud segmentation. PolarMix, a data augmentation algorithm for 3D LiDAR point cloud datasets, addresses this issue by rotating and pasting selected class instances around the Z axis multiple times to enrich the distribution of the point cloud. However, PolarMix does not adequately consider the substantial variations in instance counts within the original point clouds, leading to an imbalance in the dataset. To address this limitation, we propose a modified version of PolarMix’s instance-level rotation and pasting method that dynamically adjusts the number of rotations and pastes based on the proportion of each instance’s point cloud count relative to the total. This adaptive class-balancing approach ensures a more balanced distribution of instances across the entire dataset. We term our new algorithm Dynamic Adaptive Class-Balanced PolarMix (DACB-PolarMix). Experimental results demonstrate the effectiveness of DACB-PolarMix in balancing class distribution and enhancing model performance. The results on the SemanticKitti dataset are particularly significant. Under the MinkNet model, our method improved the mIoU from 65% to 67.9%, and under the SPVCNN model, our method increased the mIoU from 66.2% to 67.5%.

To addresses the security and efficiency problem in the Internet of Vehicles (IoV) ecosystem, where vast data exchanges between vehicles and roadside units via smart devices pose potential security, privacy, and data integrity risks. To combat these issues and improve user experience without compromising data quality, a cutting-edge framework that integrates Graph Neural Network with federated learning is proposed. This solution not only secures vehicular data privacy and integrity but also reduces unnecessary data transmissions by enabling local data storage, thus easing the load on communication networks. Additionally, I introduce a consensus mechanism powered by edge computing to minimize latency and enhance data exchange reliability, leveraging distributed computing to mitigate network congestion and scale the IoV infrastructure efficiently. Central to our framework is a reputation system that assesses user contributions, incentivizing honesty and reliability while deterring malicious activities. Our approach promises a more secure, efficient, and scalable IoV environment, showcasing the pivotal role of advanced technologies in addressing current data exchange challenges.