Geometric Primitives in LiDAR Point Clouds: A Review

Abstract

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.

Cutting Plane Based Cylinder Fitting Method With Incomplete Point Cloud Data for Digital Fringe Projection

“…Therefore, the purpose of point cloud filtering is to filter outliers and noise points as much as possible [34][35][36], reducing the influence of g S and e β on point cloud fitting, so as to imp rove the integrity of the geometric characteristics of the object itself. Aiming at the problems of outliers and noise in raw point cloud obtained by LiDA R [37][38][39][40], outlier detection methods [41][42][43] were proposed, which has good effect on isolated discrete points and sparse cluster points, but can not be applied for noise points attached to the surface of target point cloud. The point cloud obtained by LiDAR has the characteristics of large scale and low p recision, while the point cloud obtained by DFP has the characteristics of s mall scale, high precision and high density.…”

Evaluation of Convolution Operation Based on the Interpretation of Deep Learning on 3-D Point Cloud

“…Figure 8 (e) and Figure 8 (f) show a category of chairs of different types; it can be seen that the decision parts of chairs are gathered in the main skeleton, the feet of chair are confusable parts, and the pole sharp is hard to distinguish between other categories with the same sharps. From the visualization of different objects, it can infer that the common sharp parts of structure will have certain effect in the recognition ability of convolution function, it is easy confusing when just utilize single local feature, so that is why the global feature of the context should be taken into account [42].…”

Airborne LiDAR Intensity Correction Based on a New Method for Incidence Angle Correction for Improving Land-Cover Classification

“…It has been reported that intensity values in overlapping LiDAR data strips may degrade the classification accuracy [13,22,23]. Since the intensity of airborne LiDAR is affected by the system, target, and environmental parameters, the intensity of the same target in different fight lines can be very different [24]. Therefore, direct use of the original intensity values has limitations for users.…”