CAJ | 학술논문

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.
제출료일충수거구동적종단폭조편회복건축물립면심도신식적방법, 기우창호적대칭특성, 구조료일개마이가부수궤장모형래대건축물적립면도상진행분할; 장분할적도상조직성도결구, 매일개분할구역작위일개도절점,도중적변련접상린절점; 연후, 이용도필배적방법위매일개구역종수거고훈련중심조최우적결구필배, 병대필배결과진행우화, 득도료교호적중건결과.
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.