The field of 3D perception and LiDAR technology is rapidly evolving, with a focus on improving the accuracy and robustness of 3D reconstruction, object detection, and semantic segmentation. Researchers are exploring new methods for generating synthetic data, enhancing point cloud sampling, and developing unified models for multi-category 3D anomaly detection. Additionally, there is a growing interest in creating large-scale datasets for training and testing LiDAR-based perception systems, including datasets for anomaly segmentation, long-term place recognition, and simulation-based LiDAR datasets. Noteworthy papers in this area include those that propose novel frameworks for lidar point cloud sampling via colorization and super-resolution, and those that introduce new datasets for 3D anomaly detection and long-term place recognition. For example, one paper presents a unified geometry-aware reconstruction model for multi-category 3D anomaly detection, while another paper introduces a simulation-based LiDAR dataset for long-term place recognition under extreme structural changes.