Building extraction from light detection and ranging (LiDAR) data for 3-dimensional (3D) reconstruction requires accurately classified LiDAR points. In recent years, approaches developed for the classification mostly based on gridded LiDAR data. In the gridding process of LiDAR data, there is a characteristic point loss which results in reduced height accuracy. The effect of such loss can be eliminated using classified raw LiDAR data. In this study, an automatic point-based classification approach for raw LiDAR data classification with spatial features has been proposed for 3D building reconstruction. Using spatial features, the hierarchical rules have been determined. The spatial features, such as height, the local environment, and multi-return, of the LiDAR points were analyzed, and every single LiDAR points automatically assigned to the classes based on these features. The proposed classification approach based on raw LiDAR data had an overall accuracy of 79.7% in the test site located in Istanbul, Turkey. Finally, 3D building reconstruction was performed using the results of the proposed automatic point-based classification approach.

从用于3维（3D）重建的光检测和测距（LiDAR）数据中提取建筑物需要精确分类的LiDAR点。近年来，用于分类的方法主要基于栅格化的LiDAR数据。在LiDAR数据的网格化过程中，存在特征点损失，导致高度精度降低。可以使用分类的原始LiDAR数据消除这种损失的影响。在这项研究中，已经提出了一种基于点的自动分类方法，用于具有空间特征的原始LiDAR数据分类，用于3D建筑物重建。使用空间特征，已经确定了分层规则。分析了LiDAR点的高度，局部环境和多次返回等空间特征，并根据这些功能自动将每个LiDAR点分配给类别。基于原始LiDAR数据的建议分类方法在位于土耳其伊斯坦布尔的测试站点中的总体准确性为79.7％。最后，使用提出的基于点的自动分类方法的结果执行了3D建筑重建。