Effective Selection of Variable Point Neighbourhood for Feature Point Extraction from Aerial Building Point Cloud Data

Abstract

Existing approaches that extract buildings from point cloud data do not select the appropriate neighbourhood for estimation of normals on individual points. However, the success of these approaches depends on correct estimation of the normal vector. In most cases, a fixed neighbourhood is selected without considering the geometric structure of the object and the distribution of the input point cloud. Thus, considering the object structure and the heterogeneous distribution of the point cloud, this paper proposes a new effective approach for selecting a minimal neighbourhood, which can vary for each input point. For each point, a minimal number of neighbouring points are iteratively selected. At each iteration, based on the calculated standard deviation from a fitted 3D line to the selected points, a decision is made adaptively about the neighbourhood. The selected minimal neighbouring points make the calculation of the normal vector accurate. The direction of the normal vector is then used to calculate the inside fold feature points. In addition, the Euclidean distance from a point to the calculated mean of its neighbouring points is used to make a decision about the boundary point. In the context of the accuracy evaluation, the experimental results confirm the competitive performance of the proposed approach of neighbourhood selection over the state-of-the-art methods. Based on our generated ground truth data, the proposed fold and boundary point extraction techniques show more than 90% F1-scores.

Automatic Filtering of Lidar Building Point Cloud in Case of Trees Associated to Building Roof

“…To calculate the normal vectors and roof features of the noisy point cloud, the approach suggested by Dey et al [27] was adopted. This selection used the dynamic method for selecting the neighborhood of each point.…”

“…The approach suggested by Dey et al [27] starts by selecting the initial minimum number of neighboring points by considering the case of a regular distribution of points where each point has eight neighboring points. To reliably calculate a normal vector to a plane, the point selection needs an evenly distributed sample of points from the plane.…”

“…In this context, an iteration loop is employed to increase the number of neighboring points in each iteration until the selected neighboring points are located on more than one scanning line. For more details about this algorithm, please see Kumar Dey et al [27].…”

“…This matrix consists of n rows (n is the number of points of noisy roof cloud) and N columns. The number N represents the maximum possible number of neighboring points (please see Dey et al [27]). Furthermore, In the point cloud list, the order of each point is the point number, e.g., if the point number i has 45 neighboring points, then the first 45 entries of the row number i will contain the neighboring point numbers, and the rest of cells of this row will contain zeros.…”

3D Instance Segmentation and Object Detection Framework Based on the Fusion of Lidar Remote Sensing and Optical Image Sensing

“…Attribute-based point cloud segmentation algorithms are not constrained by the spatial relationship of the point cloud, and use the feature attributes [17][18][19] in the feature space to robustly cluster the feature vectors of the point cloud. For example, Holz et al [20] proposed a high frame rate real-time segmentation algorithm that uses integral images to cluster the points of local surface normal vectors and can be used to sense and detect obstacles in robot navigation scenarios.…”