RANdom SAmple Consensus (RANSAC) is a widely adopted method for LiDAR point cloud segmentation because of its robustness to noise and outliers. However, RANSAC has a tendency to generate false segments consisting of points from several nearly coplanar surfaces. To address this problem, we formulate the weighted RANSAC approach for the purpose of point cloud segmentation. In our proposed solution, the hard threshold voting function which considers both the point-plane distance and the normal vector consistency is transformed into a soft threshold voting function based on two weight functions. To improve weighted RANSAC’s ability to distinguish planes, we designed the weight functions according to the difference in the error distribution between the proper and improper plane hypotheses, based on which an outlier suppression ratio was also defined. Using the ratio, a thorough comparison was conducted between these different weight functions to determine the best performing function. The selected weight function was then compared to the existing weighted RANSAC methods, the original RANSAC, and a representative region growing (RG) method. Experiments with two airborne LiDAR datasets of varying densities show that the various weighted methods can improve the segmentation quality differently, but the dedicated designed weight functions can significantly improve the segmentation accuracy and the topology correctness. Moreover, its robustness is much better when compared to the RG method.

Keywords: Oblique aerial image, Building façade, Partition, Hierarchical repetition, Joint reconstruction In this paper we introduce an approach for automatic recognition and reconstruction of building facade structure from oblique aerial images. Contrast to street-view image oblique aerial image has larger field of view but lower resolution, weaker texture and more noise. To overcome these shortcomings, our approach firstly analyses the horizontal distribution density to extract individual facade area from image. Then a hierarchical repetition detection method is employed to partition the facade and recognize structural elements. Finally, the geometry structure of each facade element is reconstructed jointly by all repetitive image tiles. Results show the potentials of the proposed approach.