This study had two main aims: (1) to provide a comprehensive review of terrestrial laser scanner (TLS) point cloud registration methods and a better understanding of their strengths and weaknesses; and (2) to provide a large-scale benchmark data set (Wuhan University TLS: Whu-TLS) to support the development of cutting-edge TLS point cloud registration methods, especially deep learning-based methods. In particular, we first conducted a thorough review of TLS point cloud registration methods in terms of pairwise coarse registration, pairwise fine registration, and multiview registration, as well as analyzing their strengths, weaknesses, and future research trends. We then reviewed the existing benchmark data sets (e.g., ETH Dataset and Robotic 3D Scanning Repository) for TLS point cloud registration and summarized their limitations. Finally, a new benchmark data set was assembled from 11 different environments (i.e., subway station, high-speed railway platform, mountain, forest, park, campus, residence, riverbank, heritage building, underground excavation, and tunnel environments) with variations in the point density, clutter, and occlusion. In addition, we summarized future research trends in this area, including auxiliary data-guided registration, deep learning-based registration, and multi-temporal point cloud registration.

Registration of large-scale terrestrial laser scanner point clouds: A review and benchmark

Three-dimensional (3-D) urban models are an integral part of numerous applications, such as urban planning and performance simulation, mapping and visualization, emergency response training and entertainment, among others. We consolidate various algorithms proposed for reconstructing 3-D models of urban objects from point clouds. Urban models addressed in this review include buildings, vegetation, utilities such as roads or power lines and free-form architectures such as curved buildings or statues, all of which are ubiquitous in a typical urban scenario. While urban modeling, building reconstruction, in particular, clearly demand specific traits in the models, such as regularity, symmetry, and repetition; most of the traditional and state-of-the-art 3-D reconstruction algorithms are designed to address very generic objects of arbitrary shapes and topology. The recent efforts in the urban reconstruction arena, however, strive to accommodate the various pressing needs of urban modeling. Strategically, urban modeling research nowadays focuses on the usage of specialized priors, such as global regularity, Manhattan-geometry or symmetry to aid the reconstruction, or efficient adaptation of existing reconstruction techniques to the urban modeling pipeline. Aimed at an in-depth exploration of further possibilities, we review the existing urban reconstruction algorithms, prevalent in computer graphics, computer vision and photogrammetry disciplines, evaluate their performance in the architectural modeling context, and discuss the adaptability of generic mesh reconstruction techniques to the urban modeling pipeline. In the end, we suggest a few directions of research that may be adopted to close in the technology gaps.

LiDAR Point Clouds to 3-D Urban Models$:$ A Review

Mobile Laser Scanning (MLS) is a versatile remote sensing technology based on Light Detection and Ranging (lidar) technology that has been utilized for a wide range of applications. Several previous reviews focused on applications or characteristics of these systems exist in the literature, however, reviews of the many innovative data processing strategies described in the literature have not been conducted in sufficient depth. To this end, we review and summarize the state of the art for MLS data processing approaches, including feature extraction, segmentation, object recognition, and classification. In this review, we first discuss the impact of the scene type to the development of an MLS data processing method. Then, where appropriate, we describe relevant generalized algorithms for feature extraction and segmentation that are applicable to and implemented in many processing approaches. The methods for object recognition and point cloud classification are further reviewed including both the general concepts as well as technical details. In addition, available benchmark datasets for object recognition and classification are summarized. Further, the current limitations and challenges that a significant portion of point cloud processing techniques face are discussed. This review concludes with our future outlook of the trends and opportunities of MLS data processing algorithms and applications.

Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review.

The integration of multi-platform, multi-angle, and multi-temporal LiDAR data has become important for geospatial data applications. This paper presents a comprehensive review of LiDAR data registration in the fields of photogrammetry and remote sensing. At present, a coarse-to-fine registration strategy is commonly used for LiDAR point clouds registration. The coarse registration method is first used to achieve a good initial position, based on which registration is then refined utilizing the fine registration method. According to the coarse-to-fine framework, this paper reviews current registration methods and their methodologies, and identifies important differences between them. The lack of standard data and unified evaluation systems is identified as a factor limiting objective comparison of different methods. The paper also describes the most commonly-used point cloud registration error analysis methods. Finally, avenues for future work on LiDAR data registration in terms of applications, data, and technology are discussed. In particular, there is a need to address registration of multi-angle and multi-scale data from various newly available types of LiDAR hardware, which will play an important role in diverse applications such as forest resource surveys, urban energy use, cultural heritage protection, and unmanned vehicles.

Registration of Laser Scanning Point Clouds: A Review

A review of 3D reconstruction techniques in civil engineering and their applications

Terrestrial laser scanning (TLS) and mobile laser scanning (MLS) data can be used to obtain abundant and precise side information of trees. Therefore, it can enable extracting individual tree parameters, such as the tree height, crown size, crown base height, and diameter at breast height, and it can provide basic data for forest research and management. This study proposes a technical framework for segmenting individual trees from TLS and MLS data. This framework contains six steps: 1) data preprocessing, 2) octree construction, 3) spatial clustering, 4) stem detection, 5) initial segmentation, and 6) overlapped canopy segmentation. This framework makes two main contributions: 1) a top-down hierarchical segmentation approach, including connectivity-based spatial clustering (regional scale), stem-based initial segmentation (individual tree scale), and fine segmentation of overlapped canopy (canopy scale), is proposed to reduce technical difficulties and improve process efficiency; and 2) a modified node similarity calculation for normalized cut method aiming at segmenting overlapped canopy, which can effectively separate neighboring trees even if their canopies are overlapped, is proposed. The proposed framework was tested on a leaves-off terrestrial LiDAR dataset and a leaves-on mobile LiDAR dataset. For terrestrial LiDAR data, our framework achieved completeness of 92.4%, correctness of 95.4%, and F-score of 0.94. For mobile LiDAR data, the corresponding values were 94.0%, 93.7%, and 0.94.

Segmentation of Individual Trees From TLS and MLS Data

Information extraction and three-dimensional (3D) reconstruction of buildings using the vehicle-borne laser scanning (VLS) system is significant for many applications. Extracting LiDAR points, from VLS, belonging to various types of building in large-scale complex urban environments still retains some problems. In this paper, a new technical framework for automatic and efficient building point extraction is proposed, including three main steps: (1) voxel group-based shape recognition; (2) category-oriented merging; and (3) building point identification by horizontal hollow ratio analysis. This article proposes a concept of “voxel group” based on the voxelization of VLS points: each voxel group is composed of several voxels that belong to one single real-world object. Then the shapes of point clouds in each voxel group are recognized and this shape information is utilized to merge voxel group. This article puts forward a characteristic nature of vehicle-borne LiDAR building points, called “horizontal hollow ratio”, for efficient extraction. Experiments are analyzed from two aspects: (1) building-based evaluation for overall experimental area; and (2) point-based evaluation for individual building using the completeness and correctness. The experimental results indicate that the proposed framework is effective for the extraction of LiDAR points belonging to various types of buildings in large-scale complex urban environments.

Building Point Detection from Vehicle-Borne LiDAR Data Based on Voxel Group and Horizontal Hollow Analysis

Accurate and timely updated three-dimensional (3-D) model data of interchange bridges are significant for traffic navigation, landscape design, and many other applications. In this study, we explore the potential of using airborne light detection and ranging (LiDAR) data for 3-D reconstruction of large multilayer interchange bridges. To reduce the technical difficulty in this 3-D reconstruction process, we propose a concept of “structure unit.” We propose a new technical framework based on the structure units for 3-D reconstruction of large multilayer interchange bridge, including point cloud extraction, connectivity-based segmentation, determination of structure units, occlusion detection and restoration, and 3-D modeling. The general idea of this framework is to divide an entire interchange bridge into many simple “structure units,” and then derive a complete 3-D model by reconstructing each “structure unit.” The technical novelties in this framework include: 1) a hierarchal segmentation strategy including connectivity-based segmentation and determination of structure units; and 2) an automatic technique for detection and restoration of occluded structures. The experiments provide specific analysis and evaluation from three aspects: 1) correctness and completeness; 2) horizontal accuracy; and 3) elevation accuracy. The experimental results indicate that the proposed framework can provide satisfactory 3-D models of large complex bridges.

Three-Dimensional Reconstruction of Large Multilayer Interchange Bridge Using Airborne LiDAR Data

LiDAR point registration is a key procedure for the acquisition of complete point cloud datasets. It has great significance for the fusion of multisource LiDAR data. In general, the widely used methods for LiDAR point registration can be categorized into three types: auxiliary methods, direct methods, and feature methods. However, for the registration of complex objects (e.g., stadium and tower), such methods may face varying degrees of technical problems owing to the unavailability of auxiliary data or targets, requirement of sufficient overlapping areas, and difficulty in feature extraction and matching. In the real world, numerous objects with extremely complicated geometric shapes have the characteristic of symmetry. This study focuses on complex objects with symmetry and tries to exploit their intrinsic symmetry characteristic in order to facilitate their point cloud registration. A symmetry-based method for LiDAR point registration is proposed, in which the general idea is to derive 3-D central axes from multisource point clouds, based on the symmetry of objects. The proposed method consists of six main steps: detection of rotational symmetry, adaptive point cloud slicing, central point extraction, central axis fitting, central axis matching, and orientation and positioning. Comparative experiments and quantitative evaluations are conducted. The experimental results indicate that the proposed framework can achieve satisfactory registration of objects with rotational symmetry.

Yang Wu

Papers

Registration of Laser Scanning Point Clouds: A Review

Segmentation of Individual Trees From TLS and MLS Data

Building Point Detection from Vehicle-Borne LiDAR Data Based on Voxel Group and Horizontal Hollow Analysis

Three-Dimensional Reconstruction of Large Multilayer Interchange Bridge Using Airborne LiDAR Data

A Symmetry-Based Method for LiDAR Point Registration