Detecting Building-level Changes of a City Using Street Images and a 2D City Map
Detecting Building-level Changes of a City Using Street Images and a 2D City Map
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使用街道图像和 2D 城市地图检测城市的建筑物级别变化
DOI：
10.1109/wacv.2015.53
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Takayuki Okatani
中科院分区：
文献类型：
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作者：
Daiki Tetsuka;Takayuki Okatani
提出了一种从城市街道影像和二维地图中检测城市尺度变化的方法。使用SfM重建城市结构的点云，该方法通过将点云与从地图恢复的3D建筑结构进行匹配来估计每个建筑物的存在。存在多种困难，诸如恢复的建筑结构的不准确性、观测中的大差异以及因此个体建筑物的点云大小的大差异、以及由于潜在遮挡而导致的建筑物存在的相互依赖性。为了解决这些问题，我们开发了一个模型，点云是如何产生的SfM，建筑物墙壁的观察模型，和贪婪的迭代方法来科普相互依赖的顺序过程。我们实验性地将该方法应用于2011年袭击日本的海啸中受损的城市。结果表明了该方法的有效性。
This paper presents a method for detecting city-scale changes of a city from its street images and a 2D map. Using SfM to reconstruct point cloud of the structures of the city, the method estimates the existence of each building by matching the point cloud with the 3D building structures recovered from the map. There are multiple difficulties, such as inaccuracy of the recovered building structures, large differences in observation and thus in point cloud size of individual buildings, and mutual dependency of building existences due to potential occlusions. To solve these, we develop a model of how point cloud is generated in the sequential processes of SfM, an observation model of a building wall, and a greedy iterative approach to cope with the mutual dependency. We experimentally apply the method to the cities damaged by the tsunami that struck Japan in 2011. The results show the effectiveness of the method.