数据驱动的建筑物立面重建

摘要

提出了一种数据驱动的从单幅照片恢复建筑物立面深度信息的方法, 基于窗户的对称特性, 构造了一个马尔可夫随机场模型来对建筑物的立面图像进行分割; 将分割的图像组织成图结构, 每一个分割区域作为一个图节点,图中的边链接相邻节点; 然后, 利用图匹配的方法为每一个区域从数据库训练中寻找最优的结构匹配, 并对匹配结果进行优化, 得到了较好的重建结果.%We proposed a new data-driven method to infer depth information from a single facade image. A fa-cade is firstly segmented into several regions. By exploiting the symmetry characteristics of facade elements (i.e., windows), we segment the facade image using a Markov random field (MRF) formulation. We represent each facade by a graph, in which each graph node represents a segmented image region with consistent ap-pearance, and each graph edge encodes the spatial relationship between two distinct image regions. Then we generate a semantic label for each region by automatically matching the graph with our predefined templates in the database. Finally, we perform a global optimization process to produce the final facade model. Experiments demonstrate that our approach can generate favorable results.