Feature Line Generation and Regularization From Point Clouds

Chen, Xijiang; Yu, Kegen

doi:10.1109/tgrs.2019.2929138

Cited by 24 publications

(24 citation statements)

References 32 publications

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“…A region-growing method can connect edge points using point orientations estimated by RANSAC to constrain the growth. Chen and Yu [178] also used the angular properties of points on a projected plane to detect boundary points. In addition, the k-means clustering method is applied to identify fold edges.…”

Section: ) Segment-based Methods: Fold Edges In 3-d Point Cloudsmentioning

confidence: 99%

Geometric Primitives in LiDAR Point Clouds: A Review

Xia

Chen

Wang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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To the best of our knowledge, the most recent light detection and ranging (lidar)-based surveys have been focused only on specific applications such as reconstruction and segmentation, as well as data processing techniques based on a specific platform, e.g., mobile laser. However, in this article, lidar point clouds are understood from a new and universal perspective, i.e., geometric primitives embedded in versatile objects in the physical world. In lidar point clouds, the basic unit is the point coordinate. Geometric primitives that consist of a group of discrete points may be viewed as one kind of abstraction and representation of lidar data at the entity level. We categorize geometric primitives into two classes: shape primitives, e.g., lines, surfaces, and volumetric shapes, and structure primitives, represented by skeletons and edges. In recent years, many efforts from different communities, such as photogrammetry, computer vision, and computer graphics, have been made to finalize geometric primitive detection, regularization, and in-depth applications. Interpretations of geometric primitives from multiple disciplines try to convey the significance of geometric primitives, the latest processing techniques regarding geometric primitives, and their potential possibilities in the context of lidar point clouds. To this end, primitive-based applications are reviewed with an emphasis on object extraction and reconstruction to clearly show the significances of this article. Next, we survey and compare methods for geometric primitive extraction and then review primitive regularization methods that add real-world constrains to initial primitives. Finally, we summarize the challenges, expected applications, and describe possible future for primitive extraction methods that can achieve globally optimal results efficiently, even with disorganized, uneven, noisy, incomplete, and large-scale lidar point clouds.

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Section: ) Segment-based Methods: Fold Edges In 3-d Point Cloudsmentioning

confidence: 99%

Geometric Primitives in LiDAR Point Clouds: A Review

Xia

Chen

Wang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The classical Principal Component Analysis (PCA) can estimate the important geometric features of a point by collecting its k number of neighbours [23]. The minimal value of k needs to be chosen manually, but in practice, a single global k is often not suitable for an entire point cloud, where different objects in different regions may have different geometric structures or point densities [17,23]. A large value of k over-smooths the sharp feature points, while a small neighbourhood is more sensitive to local variations and noise [28].…”

Section: Neighbourhood Selectionmentioning

confidence: 99%

“…The normal vectors of points in a point cloud are important geometric properties that have been widely used by many authors to find the fold and boundary points, and high-quality surfaces [17,21,24,36,37]. Although there are several methods for estimating normal vectors in a point cloud, they are mainly proposed for 3D geometric models that have less noise with high point densities and most of the models contain smooth surfaces.…”

Section: Normal Vector Calculationmentioning

confidence: 99%

“…Although various definitions of the 3D object edges can be found in the literature [16][17][18], in the area of building reconstruction, many of the authors categorised the 3D edges into the boundary and fold edges [16,19]. Roof contours and facade outlines are referred to as boundary edges [19], and fold edges or sharp feature lines are defined as the intersecting edges of two or more planes [10,20].…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, the k-neighbourhood (also, known as k-nearest neighbours or k-NN) and r-neighbourhood [26] are two traditional approaches in selecting neighbours of a given point P i . The former selects k number of nearest points from P i , and the latter contains all points for which the distance to P i is less than or equal to r. Selecting the value for k or r is challenging as the local geometry of the object is unknown [17]. A higher value of k or r may reduce the impact of noise on the estimated normal but information from several classes or planes can be mixed up in one neighbourhood, thus producing a wrong estimation [27].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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