3-D Deep Feature Construction for Mobile Laser Scanning Point Cloud Registration

Zhang, Zhenxin; Sun, Lan; Zhong, Rugang; Chen, Dong; Xu, Zhihua; Wang, Cheng; Qin, Cheng‐Zhi; Sun, Hao; Li, Roujing

doi:10.1109/lgrs.2019.2910546

Cited by 33 publications

(15 citation statements)

References 31 publications

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“…With the development of sensor technology, it is much easier to acquire 3D point cloud data than before, which promotes a wide range of the related applications, including automatic drive [1][2] [3], robotics [4], urban point cloud labeling [44] [46] , large scale scene understanding [45] [47] [48] and simultaneous localization and mapping (SLAM) [5]. Thus, effective and high-level feature descriptors are demanded to meet the requirements of these modern applications.…”

Section: Introductionmentioning

confidence: 99%

GGM-Net: Graph Geometric Moments Convolution Neural Network for Point Cloud Shape Classification

Shen

et al. 2020

IEEE Access

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Geometric feature acts as an important role in point cloud shape classification tasks. Previous methods have proved that the geometric information of point clouds effectively improves the classification accuracy. Mo-Net firstly introduced geometric moments into point cloud shape classification, which, to fit the form of second order geometric moments, extends the number of input channels from three to nine. Unfortunately, similar to PointNet, Mo-Net cannot capture the local structures. To address this issue, we propose a graph geometric moments convolution neural network (GGM-Net), which learns local geometric features from geometric moments representation of a local point set. The core module of the GGM-Net is to learn features from geometric moments (termed as GGM convolution). Specifically, the GGM convolution learns point features and local features from the first and second order geometric moments of points and its local neighbors, respectively, and then combines these features by using an addition operation. In this way, a geometrical local representation about points is obtained, which leads to much surface geometry awareness and robustness. Equipped with the GGM convolution, GGM-Net, a simple end-to-end architecture, is developed to achieve a competitive accuracy on the benchmark dataset ModelNet40 and perform more efficiently in terms of memory and computational complexity.

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Section: Introductionmentioning

confidence: 99%

GGM-Net: Graph Geometric Moments Convolution Neural Network for Point Cloud Shape Classification

Shen

et al. 2020

IEEE Access

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“…Mobile laser scanning (MLS) systems collect a large number of three-dimensional (3D) road information along a vehicle's trajectory with high precision [1], and have been widely applied to base surveying [2], road and traffic engineering [3][4][5][6], urban planning and management [7], digital cities [8], forestry investigation [9], and cultural relics' protection [10]. Significant progress has been made in research on reconstructing scene models, extracting typical objects, and road surveys [11] based on MLS data.…”

Section: Introductionmentioning

confidence: 99%

Recovering Missing Trajectory Data for Mobile Laser Scanning Systems

et al. 2020

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Trajectory data are often used as important auxiliary information in preprocessing and extracting the target from mobile laser scanning data. However, the trajectory data stored independently may be lost and destroyed for various reasons, making the data unavailable for the relevant models. This study proposes recovering the trajectory of the scanner from point cloud data following the scanning principles of a rotating mirror. Two approaches are proposed from different input conditions: Ordered three-dimensional coordinates of point cloud data, with and without acquisition time. We recovered the scanner’s ground track through road point density analysis and restored the position of the center of emission of the laser based on plane reconstruction on a single scanning line. The validity and reliability of the proposed approaches were verified in the four typical urban, rural, winding, and viaduct road environments using two systems from different manufacturers. The result deviations of the ground track and scanner trajectory from their actual position were a few centimeters and less than 1 decimeter, respectively. Such an error is sufficiently small for the trajectory data to be used in the relevant algorithms.

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“…These local feature descriptors have a certain improvement on finding correct point-to-point correspondences in noisy point clouds, and outperform global feature descriptors in pairwise registration of point clouds. Moreover, there are also methods generating descriptors based on machine learning, such as [22]- [25]. However, there also exist some problems for them, such as time efficiency or space storage.…”

Section: Introduction and Related Workmentioning

confidence: 99%

A Local Feature Descriptor Based on Rotational Volume for Pairwise Registration of Point Clouds

Xiong

Biao

Wang³

et al. 2020

IEEE Access

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Aiming to problems in the pairwise registration of point clouds, such as keypoints are difficult to describe accurately, corresponding points are difficult to match accurately and convergence speed is slow due to uncertainty of initial transformation matrix, we propose a novel feature descriptor based on ratio of rotational volume to describe effectively keypoints, and on the basis of the feature descriptor, we proposed an improved coarse-to-fine registration pipeline of point clouds, in which we use coarse registration to obtain a good initial transformation matrix and then use fine registration based on a modified ICP algorithm to obtain an accurate transformation matrix. Experimental results show that our proposed feature descriptor has a good robustness to rotation, noise, scale and varying mesh resolution, less storage space and faster running speed than PFH, FPFH, SHOT and RoPS descriptors, and our improved pairwise registration pipeline is very effective to solve the problems in the pairwise registration of point clouds.

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3-D Deep Feature Construction for Mobile Laser Scanning Point Cloud Registration

Cited by 33 publications

References 31 publications

GGM-Net: Graph Geometric Moments Convolution Neural Network for Point Cloud Shape Classification

GGM-Net: Graph Geometric Moments Convolution Neural Network for Point Cloud Shape Classification

Recovering Missing Trajectory Data for Mobile Laser Scanning Systems

A Local Feature Descriptor Based on Rotational Volume for Pairwise Registration of Point Clouds

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