Learning high-level features by fusing multi-view representation of MLS point clouds for 3D object recognition in road environments

Luo, Zhipeng; Li, Jonathan; Xiao, Zhenlong; Mou, Zhiyu; Cai, Xiaojie; Wang, Cheng

doi:10.1016/j.isprsjprs.2019.01.024

Cited by 39 publications

(15 citation statements)

References 28 publications

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“…Third, a point belongs to the extract point sets and is not in the ground-truth point sets. Hence, the definition of precision and recall is different from the Machine Learning (ML) theory (Luo et al, 2019). Precision is denoted as the rate that the true positive (TP) points belong to the ground-truth points.…”

Section: Methodsmentioning

confidence: 99%

A Boundary-Enhanced Supervoxel Method for Extraction of Road Edges in MLS Point Clouds

Sha

Chen

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Road extraction plays a significant role in production of high definition maps (HD maps). This paper presents a novel boundary-enhanced supervoxel segmentation method for extracting road edge contours from MLS point clouds. The proposed method first leverages normal feature judgment to obtain 3D point clouds global geometric information, then clusters points according to an existing method with global geometric information to enhance the boundaries. Finally, it utilizes the neighbor spatial distance metric to extract the contours and drop out existing outliers. The proposed method is tested on two datasets acquired by a RIEGL VMX-450 MLS system that contain the major point cloud scenes with different types of road boundaries. The experimental results demonstrate that the proposed method provides a promising solution for extracting contours efficiently and completely. Results show that the precision values are 1.5 times higher and approximately equal than the other two existing methods when the recall value is 0 for both tested two road datasets.

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Section: Methodsmentioning

confidence: 99%

A Boundary-Enhanced Supervoxel Method for Extraction of Road Edges in MLS Point Clouds

Sha

Chen

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Balado et al [25] use ALS for the automatic classification of land cover using the classifier ResNet-50, and in a later work, proved the validity of PointNet for MLS point clouds' classification [26]. Although many authors are working to improve these methods [27,28], they still need to evolve. The novelty of this paper is the presentation of a fully automated method for measuring the power line gauge and the catenary deflection in a LiDAR point cloud, without the need of human intervention.…”

Section: Methodsmentioning

confidence: 99%

Automated Inspection of Railway Tunnels’ Power Line Using LiDAR Point Clouds

et al. 2019

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Transport networks need periodic inspections to increase their safety and improve their management. In the last few years, LiDAR (light detection and ranging) technology has become a tool for helping to create a precise database of almost any type of infrastructure. Mobile laser scanning (MLS) systems use a laser beam to collect dense three dimensional (3D) point clouds, which include geometric and radiometric data of the environment in which they are placed. In the context of this paper, a methodology for automatically inspecting the clearance gauge and the deflection of the aerial contact line in railway tunnels is presented. The main objective is to compare results and verify their compliance with the Spanish norm. The 3D data are provided by a LYNX Mobile Mapper System (MMS). First, the area is surveyed and then the obtained (3D) point cloud is classified into contact wire, suspension wire, and remaining points. Finally, the inspection of the railway’s power line is performed. The validation of the proposed methodology has been carried out in three different tunnel point clouds, obtaining both qualitative and quantitative results for points’ classification, together with the results of the measures performed.

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“…Field surveys with mobile LiDAR are efficient and can cover large areas that are impractical to conduct with TLSs. These key benefits have stimulated the interest of the research/professional community to apply mobile LiDAR for analyzing complex road environments, such as lane marking detection and road boundary extraction [19][20][21][22], as well as mapping railroads and tunnels [23][24][25][26]. Several studies have validated and reported the accuracy of mobile LiDAR data for monitoring civil infrastructure.…”

Section: Related Work 21 Lidar For Infrastructure Mappingmentioning

confidence: 99%

Processing Strategy and Comparative Performance of Different Mobile LiDAR System Grades for Bridge Monitoring: A Case Study

Lin

Liu

Cheng

et al. 2021

Sensors

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Collecting precise as-built data is essential for tracking construction progress. Three-dimensional models generated from such data capture the as-is conditions of the structures, providing valuable information for monitoring existing infrastructure over time. As-built data can be acquired using a wide range of remote sensing technologies, among which mobile LiDAR is gaining increasing attention due to its ability to collect high-resolution data over a relatively large area in a short time. The quality of mobile LiDAR data depends not only on the grade of onboard LiDAR scanners but also on the accuracy of direct georeferencing information and system calibration. Consequently, millimeter-level accuracy is difficult to achieve. In this study, the performance of mapping-grade and surveying-grade mobile LiDAR systems for bridge monitoring is evaluated against static laser scanners. Field surveys were conducted over a concrete bridge where grinding was required to achieve desired smoothness. A semi-automated, feature-based fine registration strategy is proposed to compensate for the impact of georeferencing and system calibration errors on mobile LiDAR data. Bridge deck thickness is evaluated using surface segments to minimize the impact of inherent noise in the point cloud. The results show that the two grades of mobile LiDAR delivered thickness estimates that are in agreement with those derived from static laser scanning in the 1 cm range. The mobile LiDAR data acquisition took roughly five minutes without having a significant impact on traffic, while the static laser scanning required more than three hours.

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Learning high-level features by fusing multi-view representation of MLS point clouds for 3D object recognition in road environments

Cited by 39 publications

References 28 publications

A Boundary-Enhanced Supervoxel Method for Extraction of Road Edges in MLS Point Clouds

A Boundary-Enhanced Supervoxel Method for Extraction of Road Edges in MLS Point Clouds

Automated Inspection of Railway Tunnels’ Power Line Using LiDAR Point Clouds

Processing Strategy and Comparative Performance of Different Mobile LiDAR System Grades for Bridge Monitoring: A Case Study

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