Intact Planar Abstraction of Buildings via Global Normal Refinement from Noisy Oblique Photogrammetric Point Clouds

Abstract

Oblique photogrammetric point clouds are currently one of the major data sources for the three-dimensional level-of-detail reconstruction of buildings. However, they are severely noise-laden and pose serious problems for the effective and automatic surface extraction of buildings. In addition, conventional methods generally use normal vectors estimated in a local neighborhood, which are liable to be affected by noise, leading to inferior results in successive building reconstruction. In this paper, we propose an intact planar abstraction method for buildings, which explicitly handles noise by integrating information in a larger context through global optimization. The information propagates hierarchically from a local to global scale through the following steps: first, based on voxel cloud connectivity segmentation, single points are clustered into supervoxels that are enforced to not cross the surface boundary; second, each supervoxel is expanded to nearby supervoxels through the maximal support region, which strictly enforces planarity; third, the relationships established by the maximal support regions are injected into a global optimization, which reorients the local normal vectors to be more consistent in a larger context; finally, the intact planar surfaces are obtained by region growing using robust normal and point connectivity in the established spatial relations. Experiments on the photogrammetric point clouds obtained from oblique images showed that the proposed method is effective in reducing the influence of noise and retrieving almost all of the major planar structures of the examined buildings.

An Automatic Hierarchical Clustering Method for the LiDAR Point Cloud Segmentation of Buildings via Shape Classification and Outliers Reassignment

“…Regarding the discriminant conditions, the geometric properties of primitives such as Euclidean distance and normal vector are commonly used [17]. Moreover, there are some studies that aim to improve the region growth in other ways; for example, Zhu et al [18] optimized the point cloud normal vectors, especially around the sharp features. Thus, the region-growing method can extract the planar structures of buildings completely.…”

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

“…In the case of buildings in a typical urban environment, the situation is complex. LiDAR data often have low point density and nonuniform sampling rate and contain a lot of noise [25]. Therefore, the accurate calculation of normal vectors in this situation is challenging.…”

“…The PCA and its variations, for example, the Weighted PCA [39], Moving Least Square (MLS) Projection [40], Robust PCA (RPCA) [41], and Diagnostic-Robust PCA (DRPCA) [42], were used by different authors for calculating normal vectors by finding the best fitted plane. Considering specifically the oblique building point cloud data in urban environments, Zhu et al [25] proposed an effective normal estimation method to handle the noise in building a point cloud through a local to global optimisation strategy. Instead of calculating the normal of individual points, they proceeded in a hierarchical fashion and merged similar points into supervoxels considering a planarity constraint to exclude outliers.…”

“…However, estimation of the normal vector along a building edge highly depends on the neighbourhood employed for each point [21,23], and the inharmonious nature of LiDAR point cloud makes the calculation of that neighbourhood complex and challenging [22,24]. Moreover, noise associated in the oblique point cloud data can create serious problems for calculating accurate normals in the context of affective and automatic building plane reconstruction [25].…”

Rock Mass Discontinuity Extraction Method Based on Multiresolution Supervoxel Segmentation of Point Cloud

“…As well known, it is hard to retrieve sufficient structure information from a single supervoxel because its construction is an under representation process [47]. To solve this problem, the individual discontinuity that encloses larger contextual information needs to be extracted.…”