LiDAR Segmentation Benchmark

LiDAR segmentation benchmarks evaluate the performance of algorithms that classify individual points in 3D LiDAR scans into semantic categories (e.g., car, pedestrian, road). Current research focuses on improving accuracy and efficiency, particularly for challenging scenarios like occluded objects and sparse data, using techniques such as multi-modal fusion (combining LiDAR with camera data), novel network architectures (e.g., frustum-based and range-view networks), and knowledge distillation to create smaller, faster models. These advancements are crucial for autonomous driving, robotics, and other applications requiring accurate and real-time understanding of 3D environments.