CBHE: Corner-based Building Height Estimation for Complex Street Scene Images

Building height estimation is important in many applications such as 3D city reconstruction, urban planning, and navigation. Recently, a new building height estimation method using street scene images and 2D maps was proposed. This method is more scalable than traditional methods that use high-resolution optical data, LiDAR data, or RADAR data which are expensive to obtain. The method needs to detect building rooflines and then compute building height via the pinhole camera model. We observe that this method has limitations in handling complex street scene images in which buildings overlap with each other and the rooflines are difficult to locate. We propose CBHE, a building height estimation algorithm considering both building corners and rooflines. CBHE first obtains building corner and roofline candidates in street scene images based on building footprints from 2D maps and the camera parameters. Then, we use a deep neural network named BuildingNet to classify and filter corner and roofline candidates. Based on the valid corners and rooflines from BuildingNet, CBHE computes building height via the pinhole camera model. Experimental results show that the proposed BuildingNet yields a higher accuracy on building corner and roofline candidate filtering compared with the state-of-the-art open set classifiers. Meanwhile, CBHE outperforms the baseline algorithm by over 10% in building height estimation accuracy.

翻译：建筑高度估计在许多应用中很重要, 比如 3D 城市重建、城市规划和导航。最近, 提出了一个新的建筑高度估计方法, 使用街头景象图像和 2D 地图。这个方法比使用高分辨率光学数据、 LiDAR 数据、或 RADAR 数据( 获取成本昂贵) 的传统方法更具有可伸缩性。这个方法需要检测建筑屋顶线, 然后通过针孔相机模型计算建筑高度。我们观察到, 这个方法在处理复杂的街道景象图像方面存在局限性, 建筑物相互重叠, 屋顶线也难以定位。我们建议CBHE, 建筑高度估计算法, 既考虑建筑角, 也考虑屋顶线。 CBHE 首次在2D 地图和相机参数的建筑足迹上找到建筑角落和屋顶线候选人。然后, 我们用名为建筑网的深神经网络来分类和过滤角落和屋顶线候选人。基于建筑网的有效角落和屋顶线, CBHE, 通过针眼相机模型, 实验结果显示, 拟议建筑网能在建筑角落和屋顶标准标准上,,, 标准标准标准, 标准, 标准标准, 标准标准标准, 标准标准, 标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准标准