HySoLiD-Drive: A Labeled Ground and Road Segmentation Benchmark for Hybrid Solid-State LiDAR (Seyond Falcon Kinetic)

HySoLiD-Drive is the first public, labeled ground- and road-segmentation benchmark captured with an automotive hybrid solid-state (HSS) LiDAR ( the Seyond / Innovusion Falcon Kinetic) mounted on two different vehicles. HSS sensors scan very differently from spinning ring LiDAR (non-repetitive, forward-facing, with concave/convex scan lines), and until now no public HSS benchmark for these tasks existed, so methods could only be evaluated on borrowed ring-LiDAR data. HySoLiD-Drive fills that gap. Contents:- 100 labeled evaluation frames, balanced 50/50 across two vehicles (different sensor height and tilt), ~73,000--95,000 points per frame (mean 88000; 8.8 million labeled points total). Distributed as full-field ASCII PLY preserving all original sensor fields (x, y, z, timestamp, intensity, flags, elongation, scan_id, scan_idx, second-return) plus a per-point label.- 7,000 unlabeled continuous frames from two real-traffic drives (one peri-urban, one open-road + urban; 3,500 each), distributed as compressed NPZ, for geometric, self-/semi-supervised and domain-adaptation research and to reproduce the timing benchmark. Labels (4 classes): 0 = non-ground, 1 = ground (non-road), 2 = other road/carriageway (visible but not ego-reachable), 3 = ego road. Two evaluation tasks are derived: ground segmentation (label >= 1) and ego-road segmentation (label == 3, with class 2 excluded from the metric as an ignore region). All point clouds are provided in an ego-centered, gravity-aligned frame (per-vehicle pitch and roll levelled). No imagery, no GPS or global coordinates, no personal data. Documentation, evaluation code (per-point IoU / Precision / Recall / F1 scorer and data loader), reference baseline results and the leaderboard are on GitHub: https://github.com/martinpalor/HySoLiD-Drive License: data CC BY-NC-SA 4.0; evaluation code MIT. The dataset accompanies the method GHySSeg (Real-Time Geometric Ground and Road Segmentation for Hybrid Solid-State LiDAR).