关键词: 立体匹配, 平面视差层, 马尔科夫随机场, 统计学习

Abstract: Focusing on scenes of outdoor buildings, the authors present graph-based stereo matching methods that incorporate depth cues to acquire more accurate disparity maps. Firstly, given a portion of scan data, planar disparity layers, which correspond to 3D planes, are precisely extracted to compose the label set. After that, the graph-based matching algorithmcan be formulated in the color segment domain instead of pixel domain. Furthermore,in order to avoid the inconvenient scanning for every scene, the scanner is only used to create a training set consisting of image-depthmap pairs. Then the depth cues, predicted through training a Markov Random Field to model the relationship between the depth and the image features, can still serve as extra constraint for the correspondence problem. The experimental results show the convincing performance of proposed methods in the reconstruction of different scenes.

Key words: stereo matching, planar disparity layer, Markov RandomField, statistical learning