TerraVehicle: A Million‐Point‐Per‐Vehicle Dataset for Fine‐Grained Component Segmentation

ABSTRACT

Current vehicle point cloud datasets are challenged by incomplete geometric details, low annotation accuracy, and limited model diversity, which are difficult to apply to high‐precision component segmentation tasks. So, a centimetre‐accurate million‐point‐per‐vehicle point cloud dataset for fine‐grained component segmentation is developed, which is named TerraVehicle and is captured by six high‐precision LiDARs. TerraVehicle employs a novel annotation strategy based on topology‐constrained segmentation primitives, surpassing traditional 3D bounding boxes, and achieves 10x greater precision compared to KITTI, nuScenes, and A* 3D. The benchmark tests against ShapeNetPart show that classification‐level mIoU scores for PointNet++, Point‐Mamba, Point‐PlaneNet, and Point2Vec on TerraVehicle remained competitive, with some models exhibiting performance improvements. Furthermore, instance‐level mIoU scores for PointNet++, Point‐PlaneNet, and Point2Vec on TerraVehicle outperform those on ShapeNetPart, which highlights that the proposed TerraVehicle is very suitable for high‐precision segmentation tasks. The experimental results validate the proposed TerraVehicle as a high‐quality benchmark for advancing the development of point cloud component segmentation algorithms.