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

Dong, Zhen; Xu, Yangsheng; Zang, Yufu; Li, Jianping; Wang, Yuan; Dai, Wei; Fan, Hongchao; Hyyppä, Juha; Stilla, Uwe

doi:10.1016/j.isprsjprs.2020.03.013

Cited by 287 publications

(153 citation statements)

References 100 publications

Supporting

Mentioning

109

Contrasting

Unclassified

Order By: Relevance

“…Wuhan University TLS (Whu-TLS) [135] was developed by Wuhan University. It consists of 115 scans and over 1.74 × 10 9 3D points in total collected from 11 different environments (i.e., a subway station, high-speed railway platform, mountain, forest, park, campus, residence, riverbank, heritage building, underground excavation and tunnel) with varying point densities, clutter, and occlusion.…”

Section: Whu-tlsmentioning

confidence: 99%

“…It consists of 115 scans and over 1.74 × 10 9 3D points in total collected from 11 different environments (i.e., a subway station, high-speed railway platform, mountain, forest, park, campus, residence, riverbank, heritage building, underground excavation and tunnel) with varying point densities, clutter, and occlusion. 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]. 1.94 × 10 5 RGB-D images 3DMatch [105] 62 indoor scenes Semantic3D [126] Over 4 billion points 8 MIMP [110] Over 5.14 × 10 7 points KITTI odometyr [111] 22 sequences Semantic KITTI [115] 22 sequences 28 ASL Dataset [116] 8 sequences iQmulus [119] 3.00 × 10 8 points 50 LiDAR…”

Section: Whu-tlsmentioning

confidence: 99%

See 1 more Smart Citation

Review: Deep Learning on 3D Point Clouds

et al. 2020

View full text Add to dashboard Cite

A point cloud is a set of points defined in a 3D metric space. Point clouds have become one of the most significant data formats for 3D representation and are gaining increased popularity as a result of the increased availability of acquisition devices, as well as seeing increased application in areas such as robotics, autonomous driving, and augmented and virtual reality. Deep learning is now the most powerful tool for data processing in computer vision and is becoming the most preferred technique for tasks such as classification, segmentation, and detection. While deep learning techniques are mainly applied to data with a structured grid, the point cloud, on the other hand, is unstructured. The unstructuredness of point clouds makes the use of deep learning for its direct processing very challenging. This paper contains a review of the recent state-of-the-art deep learning techniques, mainly focusing on raw point cloud data. The initial work on deep learning directly with raw point cloud data did not model local regions; therefore, subsequent approaches model local regions through sampling and grouping. More recently, several approaches have been proposed that not only model the local regions but also explore the correlation between points in the local regions. From the survey, we conclude that approaches that model local regions and take into account the correlation between points in the local regions perform better. Contrary to existing reviews, this paper provides a general structure for learning with raw point clouds, and various methods were compared based on the general structure. This work also introduces the popular 3D point cloud benchmark datasets and discusses the application of deep learning in popular 3D vision tasks, including classification, segmentation, and detection.

show abstract

Section: Whu-tlsmentioning

confidence: 99%

Section: Whu-tlsmentioning

confidence: 99%

Review: Deep Learning on 3D Point Clouds

et al. 2020

View full text Add to dashboard Cite

show abstract

“…We apply PCI to TLS point clouds of a campus scene to find pole-like objects. In the upper row of Figure 7a, the open-access point clouds provided by [33] are colored by elevation, and the pole light among roadside trees is circled by a black rectangle. In the second row of Figure 7a, pole-like objects are localized by the top-based method aided by PCI.…”

Section: Discussionmentioning

confidence: 99%

Point Cloud Inversion: A Novel Approach for the Localization of Trees in Forests from TLS Data

et al. 2021

View full text Add to dashboard Cite

Tree localization in point clouds of forest scenes is critical in the forest inventory. Most of the existing methods proposed for TLS forest data are based on model fitting or point-wise features which are time-consuming, sensitive to data incompleteness and complex tree structures. Furthermore, these methods often require lots of preprocessing such as ground filtering and noise removal. The fast and easy-to-use top-based methods that are widely applied in processing ALS point clouds are not applicable in localizing trees in TLS point clouds due to the data incompleteness and complex canopy structures. The objective of this study is to make the top-based methods applicable to TLS forest point clouds. To this end, a novel point cloud transformation is presented, which enhances the visual salience of tree instances and makes the top-based methods adapting to TLS forest scenes. The input for the proposed method is the raw point clouds and no other pre-processing steps are needed. The new method is tested on an international benchmark and the experimental results demonstrate its necessity and effectiveness. Finally, the proposed method has the potential to benefit other object localization tasks in different scenes based on detailed analysis and tests.

show abstract

“…The proposed method for integrating building extraction and change detection is shown in Figure 2. Suppose that the ALS and DIM point clouds are already registered to the uniform world coordinate [47]. The proposed method is designed based on the characteristics of ALS data and DIM data.…”

Section: Methodsmentioning

confidence: 99%

An Object-Based Bidirectional Method for Integrated Building Extraction and Change Detection between Multimodal Point Clouds

2020

View full text Add to dashboard Cite

Building extraction and change detection are two important tasks in the remote sensing domain. Change detection between airborne laser scanning data and photogrammetric data is vulnerable to dense matching errors, mis-alignment errors and data gaps. This paper proposes an unsupervised object-based method for integrated building extraction and change detection. Firstly, terrain, roofs and vegetation are extracted from the precise laser point cloud, based on “bottom-up” segmentation and clustering. Secondly, change detection is performed in an object-based bidirectional manner: Heightened buildings and demolished buildings are detected by taking the laser scanning data as reference, while newly-built buildings are detected by taking the dense matching data as reference. Experiments on two urban data sets demonstrate its effectiveness and robustness. The object-based change detection achieves a recall rate of 92.31% and a precision rate of 88.89% for the Rotterdam dataset; it achieves a recall rate of 85.71% and a precision rate of 100% for the Enschede dataset. It can not only extract unchanged building footprints, but also assign heightened or demolished labels to the changed buildings.

show abstract

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

Cited by 287 publications

References 100 publications

Review: Deep Learning on 3D Point Clouds

Review: Deep Learning on 3D Point Clouds

Point Cloud Inversion: A Novel Approach for the Localization of Trees in Forests from TLS Data

An Object-Based Bidirectional Method for Integrated Building Extraction and Change Detection between Multimodal Point Clouds

Contact Info

Product

Resources

About