建筑轮廓提取是摄影测量学、遥感与计算机视觉中的基础任务。近期基于图像的方法在从高分辨率光学影像中提取矢量化轮廓方面取得了显著进展。然而，光学影像本身易受遮挡、透视畸变及残余高程位移影响，导致轮廓提取不完整或错位。此外，缺乏显式高程信息限制了其在建筑细节层次（Level of Detail）建模中的直接应用。本文提出PCFootprint，这是首个面向机载激光扫描（airborne laser scanning）点云的建筑轮廓提取大规模公开数据集。PCFootprint包含来自爱沙尼亚土地与空间发展局的33,000个瓦片，覆盖多样化的城乡地理景观；每个瓦片尺寸为128 m × 128 m，并配有与点云系统对齐的矢量化轮廓标注。该数据集另含一个3,000瓦片的跨域测试集，用于评估模型在不同地理区域间的泛化能力。我们通过评估主流方法建立了全面的基准。实验结果揭示了复杂地理空间环境中存在的若干显著挑战，包括类内差异大、数据不平衡以及噪声干扰严重。我们相信PCFootprint将推动建筑建模、城市场景理解与地理空间分析等方向的未来研究。PCFootprint数据集已公开发布于\url{https://huggingface.co/datasets/Haoyuan-Shen/PCFootprint}。

Building footprint extraction is a fundamental task in photogrammetry, remote sensing, and computer vision. Recent image-based methods have achieved remarkable progress in extracting vectorized footprints from high-resolution optical imagery. However, optical imagery inherently susceptible to occlusions, perspective distortions, and residual relief displacement, yielding incomplete or misaligned footprint extraction. Furthermore, the lack of explicit elevation information limits its direct applicability to Level of Detail building modeling. In this paper, we present PCFootprint, the first large-scale public dataset for footprint extraction from airborne laser scanning point clouds. PCFootprint comprises \num{33000} tiles derived from the Estonian Land and Spatial Development Board, covering diverse urban and rural landscapes. Each tile spans \qtyproduct{128 x 128}{\m} with systematically aligned vectorized footprints aligned to point clouds. The dataset includes a \num{3000} tiles cross-domain test set for evaluating generalization across geographic regions. We establish comprehensive benchmarks by evaluating mainstream methods. Experimental results reveal significant challenges including high intra-class variance, data imbalance, and noise across complex geospatial environments. We believe PCFootprint will advance future research in building modeling, urban scene understanding, and geospatial analysis. The PCFootprint dataset is publicly available at \url{https://huggingface.co/datasets/Haoyuan-Shen/PCFootprint}.