SemanticTHAB: A High Resolution LiDAR Dataset

The SemanticTHAB dataset is a large-scale dataset designed for semantic segmentation in autonomous driving. It contains 4,750 3D LiDAR point clouds collected from urban environments. The dataset includes labeled point clouds with 20 semantic classes, such as road, car, pedestrian, and building. It provides ground truth annotations for training and evaluating semantic segmentation algorithms, offering a real-world benchmark for 3D scene understanding in self-driving car applications. The dataset is desinged to extent the SemanticKITTI benchmark by scans of a modern high resolution LiDAR sensor (Ouster OS2-128, Rev7).SemanticTHAB数据集是专为自动驾驶场景下语义分割任务设计的大规模数据集。该数据集包含4750帧从城市环境中采集的3D LiDAR点云，所有点云均标注有20个语义类别，涵盖道路、轿车、行人、建筑物等类别。本数据集为语义分割算法的训练与评估提供了真值标注，可为自动驾驶应用中的三维场景理解任务提供真实世界基准测试集。本数据集旨在通过采用现代高分辨率LiDAR传感器（Ouster OS2-128，Rev7）采集的扫描数据，对SemanticKITTI基准测试集进行扩展。