目的 目前，点云、栅格格网及不规则三角网等建筑物检测中常用的离散机载激光雷达（LIDAR）点云数据表达方式存在模型表达复杂、算法开发困难、结果表达不准确及难以表达多返回数据等缺点。为此，针对LIDAR点云体元结构模型构建及在此基础上的建筑物检测展开研究，提出一种基于体元的建筑物检测算法。方法 首先将点云数据规则化为二值（即1、0值，分别表示体元中是否包含有激光点）3D体元结构。然后利用3D滤波算法将上述体元结构中表征数据点的体元分类为地面和非地面体元。最后，依据建筑物边缘的接近直线、跳变特性从非地面体元中搜寻建筑物边缘作为种子体元进而标记与其3D连通的非地面体元集合为建筑物体元。结果 实验基于ISPRS（international society for photogrammetry and remote sensing）提供的包含了不同的建筑物类型的城区LIDAR点云数据测试了"邻域尺度"参数的敏感性及提出算法的精度。定量评价的结果表明：56邻域为最佳邻域尺度；建筑物的检测质量可达到95%以上——平均完整度可达到95.61%、平均正确率可达95.97%。定性评价的结果表明：对大型、密集、不规则形状、高低混合及其他屋顶类型比较特殊的复杂建筑物均可成功检测。结论 本文提出的建筑物检测算法采用基于体元空间邻域关系的搜索标记方式，可有效实现对各类建筑目标特别是城市建筑目标的检测，检测结果易于建模3D建筑物模型。

Objective Automatic building detection is important for 3D city modeling.Airborne light detection and ranging(LIDAR) point cloud data are dense,georeferenced as well as 3D,they are the natural choice for 3D object detection and extraction,e.g.,building.Point cloud,raster grid,and triangulated irregular network(TIN),which are the commonly used methods to represent scattered LIDAR point cloud data for building detection,have defects;for example,their model representations are complex,and thus using the data processing algorithm is difficult,and the results are not accurate and unable to represent multiple returns LIDAR data.To overcome the restrictions of existing point-,grid-,and TIN-based approaches,this paper focuses on "establishing voxel structure model for airborne LIDAR point cloud data and developing a new building detection algorithm based on the constructed voxel model" and proposes a Voxel-based building detection(VBD) algorithm for separating buildings from non-buildings.Method The proposed VBD algorithm consists of three steps.First,LIDAR point clouds is regularized into binary 3D voxel structure,which can be obtained by dividing the entire scene volume into a 3D regular grid(the 3D sub-volumes,called voxels),remapping the LIDAR points to 3D voxels and assigning the voxel value 1 or 0 when a voxel contains LIDAR points or not.Second,the voxels with voxel value 1 are separated into ground and unground voxels utilizing a voxel-based 3D filtering algorithm.Third,a group of non-ground voxels with almost straight line and jump features are selected as building edge seed voxels and then their 3D connected set are labeled as building voxels.The proposed algorithm is based on the idea of 3D connectivity construct and is designed based on a binary voxel structure which is a simpler 3D structure,in which topological and adjacent relations between voxels can be established much easier.The advantage of the proposed algorithm lies in utilizing connectivity and hidden elevation information between voxels.Result ISPRS urban LIDAR datasets,which are representative of buildings of diverse types,are used to analyze the sensitivity of "adjacency size" parameter in the model and assess the accuracy of VBD algorithm quantitatively.The quantitative evaluation results indicate that:1) the 56-adjacency is the optimal adjacent size;2) an over 95% average quality of building-detection,achieving 95.61% average completeness and 95.97% average correctness,which report promising performance.The qualitative evaluation results indicate that large,dense,and irregularly shaped buildings or buildings with eccentric roofs are all successfully captured.The outstanding merit of the VBD algorithm based on the idea of 3D connected set is that some roof elements(e.g.,small dormers,chimneys) can also be detected.Conclusion The proposed building detection algorithm of 3D connected-component label can effectively detect all kinds of buildings,especially urban buildings.The detected building results can directly serve as building model that is a new form of 3D voxel model with certain accuracy and are easy to build a 3D building model.However,the proposed algorithm suffers from separating trees and buildings if both are adjacent to each other,thereby forming a 3D connected set.The reason for this is that the regularized binary 3D voxel data cannot integrate with multispectral,hyperspectral,or other optical data source.Optical images can provide a variety of information,such as intensity,colours and textures,which can provide detailed information for preventing the spreading of the building-regions throughout the neighboring objects,and thus further improving the accuracy of building detection.Nevertheless,we could conclude that the proposed algorithm is promising for automatic building detection.