Lidar Semantic Segmentation

Lidar semantic segmentation aims to automatically classify individual points in 3D lidar point clouds into meaningful semantic categories (e.g., car, pedestrian, road), enabling robots and autonomous vehicles to understand their surroundings. Current research focuses on improving accuracy and efficiency through various techniques, including the development of novel network architectures (e.g., transformers, encoder-decoder models) and algorithms that leverage temporal information, multi-modal data fusion (combining lidar with camera data), and active learning to reduce annotation costs. This field is crucial for advancing autonomous driving, robotics, and other applications requiring robust 3D scene understanding, with ongoing efforts concentrating on improving robustness to adverse weather conditions and sensor variations.