Recently, there has been a great demand for 3D building models in several applications including cartography and planning applications in urban areas. This led to the development of automated algorithms to extract such models since they reduce the time and cost when compared to manually onscreen digitizing. Most algorithms are built to solve the proposed problem from either LiDAR datasets or aerial imageries. Since both datasets have their weaknesses, integrating these datasets has the potential to be more successful in 3D modeling since the limitations of each source can be fulfilled by the other. In this article, we outline an algorithm that generates 3D building wireframes from LiDAR DEMs and high-resolution aerial images. Each post in the DEM is assigned five different attributes representing the intensity and elevations in its neighborhood. Posts are then classified as ground or non-ground using a feedforward back-propagation neural network. Non-ground points are grouped into different planer patches using Hough transformation, and these patches are iteratively refined using the L1-norm blunder detector and a region-growing segmentation algorithm. Finally, topological relationships among roof planes and boundary points are satisfied through regression analyses. The algorithm is tested on a number of buildings with complex rooftops, and results show its promising precision and completeness in modeling various building shapes.

近来，在包括城市制图和规划应用在内的多种应用中，对3D建筑模型的需求很大。由于与手动屏幕数字化相比，它们减少了时间和成本，因此导致了提取此类模型的自动化算法的开发。大多数算法都是为解决LiDAR数据集或航空影像中提出的问题而构建的。由于两个数据集都有其弱点，因此集成这些数据集可能会在3D建模中取得更大的成功，因为每个数据源的局限性都可以由另一个数据源来满足。在本文中，我们概述了一种从LiDAR DEM和高分辨率航拍图像生成3D建筑线框的算法。DEM中的每个帖子都分配了五个不同的属性，分别表示其附近的强度和海拔。然后使用前馈反向传播神经网络将帖子分类为地面或非地面。使用Hough变换将非地面点分组为不同的平面补丁，并使用L1范数错误检测器和区域增长分割算法对这些补丁进行迭代精炼。最后，通过回归分析满足了屋顶平面与边界点之间的拓扑关系。该算法在许多具有复杂屋顶的建筑物上进行了测试，结果表明，该算法在对各种建筑物形状进行建模时具有很高的准确性和完整性。然后使用L1范数过失检测器和区域增长分割算法对这些补丁进行迭代完善。最后，通过回归分析满足了屋顶平面与边界点之间的拓扑关系。该算法在许多具有复杂屋顶的建筑物上进行了测试，结果表明，该算法在对各种建筑物形状进行建模时具有很高的准确性和完整性。然后使用L1范数过失检测器和区域增长分割算法对这些补丁进行迭代完善。最后，通过回归分析满足了屋顶平面与边界点之间的拓扑关系。该算法在许多具有复杂屋顶的建筑物上进行了测试，结果表明，该算法在对各种建筑物形状进行建模时具有很高的准确性和完整性。