Abstract

We describe a method to automatically detect contours, i.e. lines along which the surface orientation sharply changes, in large-scale outdoor point clouds. Contours are important intermediate features for structuring point clouds and converting them into high-quality surface or solid models, and are extensively used in graphics and mapping applications. Yet, detecting them in unstructured, inhomogeneous point clouds turns out to be surprisingly diffificult, and existing line detection algorithms largely fail. We approach contour extraction as a two-stage discriminative learning problem. In the fifirst stage, a contour score for each individual point is predicted with a binary classififier, using a set of features extracted from the point’s neighborhood. The contour scores serve as a basis to construct an overcomplete graph of candidate contours. The second stage selects an optimal set of contours from the candidates. This amounts to a further binary classifification in a higher-order MRF, whose cliques encode a preference for connected contours and penalize loose ends. The method can handle point clouds > 107 points in a couple of minutes, and vastly outperforms a baseline that performs Canny-style edge detection on a range image representation of the point cloud