Urban scene modeling is a challenging task for the photogrammetry and computer vision community due to its large scale, structural complexity, and topological delicacy. This paper presents an efficient multistep modeling framework for large-scale urban scenes from aerial images. It takes aerial images and a textured 3D mesh model generated by an image-based modeling system as the input and outputs compact polygon models with semantics at different levels of detail (LODs). Based on the key observation that urban buildings usually have piecewise planar rooftops and vertical walls, we propose a segment-based modeling method, which consists of three major stages: scene segmentation, roof contour extraction, and building modeling. By combining the deep neural network predictions with geometric constraints of the 3D mesh, the scene is first segmented into three classes. Then, for each building mesh, the 2D line segments are detected and used to slice the ground into polygon cells, followed by assigning each cell a roof plane via a MRF optimization. Finally, the LOD model is obtained by extruding cells to their corresponding planes. Compared with direct modeling in 3D space, we transform the mesh into a uniform 2D image grid representation and most of the modeling work is performed in 2D space, which has the advantages of low computational complexity and high robustness. In addition, our method doesn't require any global prior, such as the Manhattan or Atlanta world assumption, making it flexible to model scenes with different characteristics and complexity. Experiments on both single buildings and large-scale urban scenes demonstrate that by combining 2D photometric with 3D geometric information, the proposed algorithm is robust and efficient in urban scene LOD vectorized modeling compared with the state-of-the-art approaches.

中文翻译：

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基于摄影测量网格的城市场景 LOD 矢量化建模


由于城市场景建模规模大、结构复杂、拓扑精细，对于摄影测量和计算机视觉界来说是一项具有挑战性的任务。本文提出了一种针对航空图像大规模城市场景的高效多步建模框架。它将航空图像和基于图像的建模系统生成的纹理 3D 网格模型作为输入，并输出具有不同细节级别 (LOD) 语义的紧凑多边形模型。基于城市建筑通常具有分段平面屋顶和垂直墙的关键观察，我们提出了一种基于分段的建模方法，该方法包括三个主要阶段：场景分割、屋顶轮廓提取和建筑建模。通过将深度神经网络预测与 3D 网格的几何约束相结合，场景首先被分为三类。然后，对于每个建筑物网格，检测 2D 线段并将其用于将地面切片为多边形单元，然后通过 MRF 优化为每个单元分配一个屋顶平面。最后，通过将单元拉伸到相应的平面来获得LOD模型。与3D空间中的直接建模相比，我们将网格转换为统一的2D图像网格表示，并且大部分建模工作在2D空间中进行，具有计算复杂度低和鲁棒性高的优点。此外，我们的方法不需要任何全局先验，例如曼哈顿或亚特兰大世界假设，从而可以灵活地对具有不同特征和复杂性的场景进行建模。 对单体建筑和大规模城市场景的实验表明，通过将 2D 光度与 3D 几何信息相结合，所提出的算法在城市场景 LOD 矢量化建模中与最先进的方法相比具有鲁棒性和高效性。