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.
Highlights
L IGHT detection and ranging is a kind of remote sensing technique, which uses light in the form of a pulsed laser to measure the distance from the sensor to the objects
This review provides an overview and a perspective on how to perceive and understand lidar point clouds based on geometric primitives
We focused on geometric primitives as core elements in lidar point clouds and examined point cloud processing from a geometric perspective
Summary
L IGHT detection and ranging (lidar) is a kind of remote sensing technique, which uses light in the form of a pulsed laser to measure the distance from the sensor to the objects. From the perspective of surveying principles, the existing lidar systems can be categorized into time-of-flight and phase-shift systems. The former measures distance by recording the transmission time of light between the laser sensor and objects, while the latter uses the phase difference between emitted and backscattered signals to determine the measure distance. Ground-based lidar’s ability to acquire precise high-resolution 3-D details has become an attractive technology for environmental mapping, including pavement identification [2], [3], road marker extraction [4], [5], curb modeling [6], [7], vegetation mapping [8], [9], pole-like object localization [10], [11], vehicle detection [12], [13], and building reconstruction [14], [15]. Ground-based lidar’s ability to acquire precise high-resolution 3-D details has become an attractive technology for environmental mapping, including pavement identification [2], [3], road marker extraction [4], [5], curb modeling [6], [7], vegetation mapping [8], [9], pole-like object localization [10], [11], vehicle detection [12], [13], and building reconstruction [14], [15]. (A recent review of MLS data processing and applications can be found in [16].) gaps between lidar research and practice remain, with notable deficiencies in scalability and robustness of current processing methods
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