Training-Free Semantic Labeling of Mobile LiDAR Using Vector HD Maps for Driving Simulation
Year: 2026
Type: article
Abstract: Driving simulation needs labeled 3D scenes, but mobile mapping system (MMS) light detection and ranging (LiDAR) is often delivered unclassified, without point-wise labels for training. We present a training-free pipeline that transfers the semantics of a vector high-definition (HD) map, the Korean precision road map, onto MMS LiDAR and outputs a labeled 0.5 m voxel scene, and we analyze what limits its accuracy. Each voxel takes the label of the nearest map feature whose category-specific radius and elevation window it satisfies, with elevations measured from a ground model built from the map’s 3D vertices. The pipeline needs no training data but requires a 3D vector map. On an expressway interchange, the ground model reached a median error of 6.6 cm, on par with inverse-distance weighting (IDW), triangulated irregular network (TIN), and local-plane interpolation. A blind, stratified random sample of 695 cells and a blind review of all 91 output objects showed that 98.0% [95.8, 99.5] of the output voxels are static solid structure and 73% [63, 81] of the objects are the mapped facility; the cell-level macro-F1 over six categories was 0.44 [0.39, 0.52] (guardrail 0.73, tunnel 0.94, sign 0.14). Errors were concentrated: four tunnel-mounted signals labeled the tunnel ceiling, and the radii of signs and posts captured roadside vegetation. The results show where map-based labels are reliable for simulation and which map content needs treatment.
DOI: 10.3390/ijgi15110503
Authors: Eun-Joo Suk, Yo-Han Han, Gwang-Jae We, Ju-Hwan Moon
Language: English
Subfield: Geology
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,292
OpenAlex ID: W7225646187