A Canadian Benchmark LiDAR Dataset for Urban Infrastructure and 3D Scene Understanding

Engineering notes

The dataset includes a fine-grained taxonomy of 18 semantic classes, with an emphasis on detailed pedestrian, cyclist, and roadway infrastructure rarely distinguished in existing benchmarks. We additionally present a comprehensive baseline evaluation using five state-of-the-art semantic segmentation models, including PointNet++, DGCNN, KPConv, KPConvX, and Point Transformer v3.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

High-quality 3D perception is essential for autonomous vehicles, urban analytics, and the development of intelligent transportation systems. However, existing LiDAR datasets are limited in their representation of fine-grained roadway and pedestrian infrastructure, and geographic diversity, particularly for environments common in North American cities. This paper introduces YEG3D, a large-scale, point-wise annotated mobile laser scanning (MLS) dataset comprising more than 682 million points collected across 14 km of urban roadway in Edmonton, Canada. The dataset includes a fine-grained taxonomy of 18 semantic classes, with an emphasis on detailed pedestrian, cyclist, and roadway infrastructure rarely distinguished in existing benchmarks. We additionally present a comprehensive baseline evaluation using five state-of-the-art semantic segmentation models, including PointNet++, DGCNN, KPConv, KPConvX, and Point Transformer v3. Among the evaluated models, Point Transformer V3 achieves the strongest overall performance, attaining 81.8% overall accuracy, 46.2% mean Intersection over Union (mIoU), and 56.8% mean F1 score, outperforming all other architectures across both global and class-level metrics. Detailed confusion matrix analysis reveals that while large structural classes are segmented reliably, fine-grained elements such as markings, bike lanes, and crosswalks remain challenging due to sparsity, occlusion, and class imbalance. YEG3D provides a new foundation for advancing research in 3D semantic segmentation, urban perception, and infrastructure-aware autonomous systems, and will be expanded in future releases to broaden its geographic and semantic coverage.

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