Robust 3D Line Extraction from Stereo Point Clouds

The paper describes a robust method to extract 3D lines from stereo point clouds. This method combines 2D image information with 3D point clouds from a stereo camera. 2D lines are first extracted from the image in the stereo pair, followed by 3D line regression from the back-projected 3D point set of the images points in the detected 2D lines. In this paper, random sample consensus (RANSAC) is used to estimate 3D line from the 3D point set, the Mahalanobis distance from each 3D point to the 3D line is derived, and the statistically motivated distance measure is used to compute the support for the detected 3D line. Experimental results on real environment with high level of clutter, occlusion, and noise demonstrate the robustness of the algorithm.