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

By Z. Dong, Fuxun Liang, Bisheng Yang, Yusheng Xu, Y. Zang, Jianping Li, Yuan Wang, Wenxia Dai, Hongchao Fan, Juha Hyyppä and 1 more. 2020

383 citations recorded by the source corpus.

Abstract 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.

Subfields: Aerospace Engineering, Environmental Engineering, Geology

View Paper: https://www.semanticscholar.org/paper/03583a7a6b220b5f5ed9897ef178f160a72a0574

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Cheng Wang
2. Saifullahi Aminu Bello
3. Shangshu Yu

In Environmental Engineering

1. Cheng Wang
2. Saifullahi Aminu Bello
3. Shangshu Yu

In Geology

1. Cheng Wang
2. Saifullahi Aminu Bello
3. Shangshu Yu

Citations in the corpus

• Review: deep learning on 3D point clouds (2020, methodology): “The ground-truth transformations, the transformations calculated by [136], and the registration graphs are also provided for researchers, with the aim of yielding better comparisons and insights into the strengths and weaknesses of different registration approaches on a common basis [135].”