3D Line Segment Detection Based on Disparity Data of Area-Based Stereo

Abstract

This paper presents a new 2D line matching technique in terms of disparity data generated by area-based stereo to build 3D site model from urban images. The core of our technique is that feature matching is carried out by the reference of the disparity evaluated by area-based stereo. Since the reference of the disparity can significantly reduce the number of feature matching combinations, feature matching error can be drastically minimized. One requirement of the disparity to be referenced is that it should be reliable to be used in feature matching. To measure the reliability of the disparity, in this paper, we employ the selfconsistency of the disparity. Our suggested technique is applied to the detection of 3D line segments by 2D line matching using our hybrid stereo matching, which can be efficiently utilized in the generation of the rooftop model from urban images. The suggested method is evaluated on Avenches data set of Ascona aerial images. Experimental results indicate that the extracted 3D line segments have an average error of 0.5m and can be efficiently used to the construction of 3D site models.

3D line segment detection algorithm for large-scale scenes

“…Woo et al [5] used disparity data among images as prior information to find out lines with similar disparity distribution. Such methods could improve the matching efficiency in some cases, but need to know the epipolar geometry or disparity in advance, which is limited to the small scenes and can not be extended to large scale scene applications.…”