Survey of 3-Dimensional Point Cloud Processing Based on Deep Learning

被引：0
作者：
Li J. [1 ]
Sun H. [1 ]
Dong Y. [1 ]
Zhang R. [1 ]
Sun X. [1 ,2 ]
机构：
[1] Institute of Computer System, School of Computer and Information Technology, Liaoning Normal University, Dalian
[2] Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing
来源：
Jisuanji Yanjiu yu Fazhan/Computer Research and Development | 2022年 / 59卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Classification and segmentation; Deep learning; Detection and tracking; Point cloud; Pose estimation; Reconstruction;
D O I：
10.7544/issn1000-1239.20210131
中图分类号：
学科分类号：
摘要：
Deep learning has shown its superior performance in the structured data analysis such as 2-dimensional images. In recent years, with the development of LIDAR sensing equipment and related technologies, 3-dimensional point cloud scanning and acquisition has become more convenient. That makes the analysis and processing of unstructured point cloud data potential become an important research direction and obtain some progress in many fields such as computer graphics, robot, autonomous driving, virtual and augmented reality. A survey on the research of 3-dimensional point cloud processing of recent years is presented. Focusing on the application of deep learning in 3-dimensional point cloud shape analysis, structure extraction, detection and repair, we introduce the extraction method of point cloud topological structure, and compare the progress of the following research directions with the construction of neural networks as the main method: shape deformation, reconstruction, segmentation, classification, object tracking, scene flow estimation, object detection and pose estimation. Finally, we summarize the commonly used 3-dimensional point cloud public datasets, analyze and compare the characteristics and evaluation indicators of various point cloud processing task methods, and point out their advantages and disadvantages. The challenges and development directions of processing point cloud data based on deep learning are discussed. © 2022, Science Press. All right reserved.
引用
收藏
页码：1160 / 1179
页数：19
相关论文
共 100 条
[1]  
Charles R Q, Su Hao, Mo Kaichun, Et al., PointNet: Deep learning on point sets for 3D classification and segmentation, Proc of the 30th IEEE Conf on Computer Vision and Pattern Recognition, pp. 77-85, (2017)
[2]  
Han Kai, Wang Yunhe, Chen Hanting, Et al., A survey on visual transformer, (2021)
[3]  
LeCun Y, Bengio Y, Hinton G., Deep learning, Nature, 521, 7553, pp. 436-444, (2015)
[4]  
Lin H, Cong M N., Inference of 6-DOF robot grasps using point cloud data, Proc of the 19th Int Conf on Control, Automation and Systems, pp. 944-948, (2019)
[5]  
Khanh T T, Hoang Hai T, Nguyen V, Et al., The practice of cloud-based navigation system for indoor robot, Proc of the 14th Int Conf on Ubiquitous Information Management and Communication, (2020)
[6]  
Lin Weiyang, Anwar A, Li Zhan, Et al., Recognition and pose estimation of auto parts for an autonomous spray painting robot, IEEE Transactions on Industrial Informatics, 15, 3, pp. 1709-1719, (2019)
[7]  
Parra C, Cebollada S, Paya L, Et al., A novel method to estimate the position of a mobile robot in underfloor environments using RGB-D point clouds, IEEE Access, 8, pp. 9084-9101, (2020)
[8]  
Yang Jubo, Kong Minxiu, Wang Rundong, A path planning algorithm of spray robot based on 3D point cloud, Proc of the 2nd Int Conf on Communications, Information System and Computer Engineering, pp. 353-360, (2020)
[9]  
Hanke T, Schaermann A, Geiger M, Et al., Generation and validation of virtual point cloud data for automated driving systems, Proc of the 20th Int Conf on Intelligent Transportation Systems, (2017)
[10]  
Josyula A, Anand B, Rajalakshmi P., Fast object segmentation pipeline for point clouds using robot operating system, Proc of the 5th World Forum on Internet of Things, pp. 915-919, (2019)
12345678910