LiDAR Segmentation

LiDAR segmentation aims to automatically classify individual points in 3D LiDAR scans into semantic categories (e.g., car, pedestrian, road), enabling scene understanding for applications like autonomous driving. Current research focuses on improving accuracy and efficiency through novel architectures like range-aware networks, temporal aggregation networks, and multi-task learning frameworks that integrate LiDAR segmentation with object detection. These advancements, often incorporating data augmentation and domain adaptation techniques to address data sparsity and sensor variations, are crucial for robust and reliable perception in real-world environments.