基于激光雷达点云的3D目标检测方法综述

doi:10.11896/jsjkx.220400214

Abstract

Abstract: In recent years,3D target detection using LiDAR point cloud is a research hotspot in the field of computer vision and has attracted much attention in the field of autonomous driving.Compared with 2D,3D combines depth information to better reflect the characteristics of the real world,to effectively solve practical problems such as path planning,motion prediction,target detection,and other aspects.This paper introduces the development background of 3D target detection,summarizes the flow of 3D target detection framework based on LiDAR point cloud data,compares several common data sets containing point cloud information,and classifies the main research methods.The performance and limitations of different methods are analyzed and compared.Finally,the current technical difficulties are summarized and the future development prospects of this field are forecasted.

Key words: Target detection, Point cloud, Computer vision, LiDAR, Multimodal fusion

CLC Number:

TP391.41

QIN Jing, WANG Weibin, ZOU Qijie, WANG Zumin, JI Changqing. Review of 3D Target Detection Methods Based on LiDAR Point Clouds[J].Computer Science, 2023, 50(6A): 220400214-7.

References

[1]LIU W,ANGUELOV D,ERHAN D,et al.Ssd:Single shotmultibox detector[C]//European Conference on Computer Vision.2016:21-37.
[2]REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once:Unified,real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:779-788.
[3]REN S,HE K,GIRSHICK R,et al.Faster r-cnn:Towards real-time object detection with region proposal networks[J].Advances in Neural Information Processing systems,2015,28.
[4]MASI I,WU Y,HASSNER T,et al.Deep face recognition:A survey[C]//2018 31st SIBGRAPI Conference on Graphics,Patterns and mages(SIBGRAPI).IEEE,2018:471-478.
[5]HUANG R,GU J,SUN X,et al.A rapid recognition method for electronic components based on the improved YOLO-V3 network[J].Electronics,2019,8(8):825.
[6]YURTSEVER E,LAMBERT J,CARBALLO A,et al.A survey of autonomous driving:Common practices and emerging techno-logies[J].IEEE access,2020,8:58443-58469.
[7]LIANG M,YANG B,ZENG W,et al.Pnpnet:End-to-end perception and prediction with tracking in the loop[C]//Procee-dings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:11553-11562.
[8]XIAO Y Q,YANG H M.Research on Application of Object Detection Algorithm in Traffic Scene[J].Computer Engineering and Applications,2021,57(6):30-41.
[9]BRESSON G,ALSAYED Z,YU L,et al.Simultaneous localization and mapping:A survey of current trends in autonomousdriving[J].IEEE Transactions on Intelligent Vehicles,2017,2(3):194-220.
[10]BOJARSKI M,DEL TESTA D,DWORAKOWSKI D,et al.End to end learning for self-driving cars[J].arXiv:1604.07316,2016.
[11]HECHT J.Lidar for self-driving cars[J].Optics and Photonics News,2018,29(1):26-33.
[12]PARK Y,YUN S,WON C S,et al.Calibration between color camera and 3D LIDAR instruments with a polygonal planar board[J].Sensors,2014,14(3):5333-5353.
[13]SOMMER H,KHANNA R,GILITSCHENSKI I,et al.A low-cost system for high-rate,high-accuracy temporal calibration for LIDARs and cameras[C]//2017 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS).2017:2219-2226.
[14]CUI Y,CHEN R,CHU W,et al.Deep learning for image and point cloud fusion in autonomous driving:A review[J].IEEE Transactions on Intelligent Transportation Systems,2021,23(2):722-739.
[15]CHAZETTE P,TOTEMS J,HESPEL L,et al.Principle and physics of the LiDAR measurement[M]//Optical Remote Sen-sing of Land Surface.Elsevier,2016:201-247.
[16]HE H,WANG H,SUN L.Research on 3D point-cloud registration technology based on Kinect V2 sensor[C]//2018 Chinese Control And Decision Conference(CCDC).IEEE,2018:1264-1268.
[17]XU Y,JOHN V,MITA S,et al.3D point cloud map based vehicle localization using stereo camera[C]//2017 IEEE Intelligent Vehicles Symposium(IV).IEEE,2017:487-492.
[18]LI G Y,LI M L,WANG L,et al.AReview of Preprocessing of Laser-scanned Point Clouds Data[J].Bulletin of Surveying and Mapping,2015(11):1-3.
[19]XU Z,ZHANG Z,ZHONG R,et al.Content-sensitive multilevel point cluster construction for ALS point cloud classification[J].Remote Sensing,2019,11(3):342.
[20]RUSU R B,COUSINS S.3d is here:Point cloud library(pcl)[C]//2011 IEEE International Conference on Robotics and Automation.2011:1-4.
[21]GEIGER A,LENZ P,URTASUN R.Are we ready for autonomous driving? the kitti vision benchmarksuite[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition.2012:3354-3361.
[22]CAESAR H,BANKITI V,LANG A H,et al.nuscenes:A multimodal dataset for autonomous driving[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:11621-11631.
[23]SUN P,KRETZSCHMAR H,DOTIWALLA X,et al.Scalability in perception for autonomous driving:Waymo open dataset[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:2446-2454.
[24]QI C R,SU H,MO K,et al.Pointnet:Deep learning on point sets for 3d classification and segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:652-660.
[25]QI C R,YI L,SU H,et al.Pointnet++:Deep hierarchical feature learning on point sets in a metric space[C]//Conference and Workshop on Neural Information Processing Systems.2017.
[26]SHI S,WANG X,LI H.Pointrcnn:3d object proposal generation and detection from point cloud[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019:770-779.
[27]YANG Z,SUN Y,LIU S,et al.3dssd:Point-based 3d single stage object detector[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:11040-11048.
[28]ZHOU Y,TUZEL O.Voxelnet:End-to-end learning for point cloud based 3d object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:4490-4499.
[29]YAN Y,MAO Y,LI B.Second:Sparsely embedded convolutionaldetection[J].Sensors,2018,18(10):3337.
[30]LANG A H,VORA S,CAESAR H,et al.Pointpillars:Fast encoders for object detection from point clouds[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019:12697-12705.
[31]YE M,XU S,CAO T.Hvnet:Hybrid voxel network for lidar based 3d object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:1631-1640.
[32]SHI S,WANG Z,SHI J,et al.From points to parts:3d object detection from point cloud with part-aware and part-aggregation network[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2020,43(8):2647-2664.
[33]HE C,ZENG H,HUANG J,et al.Structure aware single-stage 3d objectdetection from point cloud[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:11873-11882.
[34]GUSTAFSSON F K,DANELLJAN M,SCHON T B.Accurate 3D object detection using energy-based models[C]//Proceedings ofthe IEEE/CVF Conference on Computer Vision and Pattern Recognition.2021:2855-2864.
[35]ZHENG W,TANG W,CHEN S,et al.CIA-SSD:Confident IoU-aware single-stage object detector from point cloud[J].arXiv:2012.03015,2020.
[36]ZHENG W,TANG W,JIANG L,et al.SE-SSD:Self-ensembling single-stage object detector from point cloud[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2021:14494-14503.
[37]LI Z,YAO Y,QUAN Z,et al.Sienet:spatial information enhancement network for 3d object detection from point cloud[J].arXiv:2103.15396,2021.
[38]CHEN Q,SUN L,WANG Z,et al.Object as hotspots:An anchor-free 3d object detection approach via firing of hotspots[C]//European Conference on Computer Vision.2020:68-84.
[39]QIAN R,LAI X,LI X.Boundary-aware 3d object detection from point clouds[J].arXiv:2104.10330,2021.
[40]LI J,DAI H,SHAO L,et al.From voxel to point:Iou-guided 3d object detection for point cloud with voxel-to-point decoder[C]//Proceedings of the 29th ACM International Conference on Multimedia.2021:4622-4631.
[41]DENG J,ZHOU W,ZHANG Y,et al.From Multi-View to Hol-low-3D:Hallucinated Hollow-3D R-CNN for 3D Object Detection[J].IEEE Transactions on Circuits and Systems for Video Technology,2021,31(12):4722-4734.
[42]XIE L,XU G,CAI D,et al.X-view:Non-egocentric Multi-View 3D Object Detector[J].arXiv:2103.13001,2021.
[43]CHEN X,MA H,WAN J,et al.Multi-view 3dobject detection network for autonomous driving[C]//Proceedings of the IEEE conference on Computer Vision and Pattern Recognition.2017:1907-1915.
[44]KU J,MOZIFIAN M,LEE J,et al.Joint 3d proposal generation and object detection from view aggregation[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS).2018:1-8.
[45]YOO J H,KIM Y,KIM J,et al.3d-cvf:Generating joint camera and lidar features using cross-view spatial feature fusion for 3d object detection[C]//European Conference on Computer Vision.2020:720-736.
[46]PANG S,MORRIS D,RADHA H.CLOCs:Camera-LiDAR object candidates fusion for 3D object detection[C]//2020 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS).2020:10386-10393.
[47]KU J,PON A D,WALSH S,et al.Improving 3d object detection for pedestrians with virtual multi-view synthesis orientation estimation[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS).2019:3459-3466.
[48]VORA S,LANG A H,HELOU B,et al.Pointpainting:Sequential fusion for 3d object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:4604-4612.
[49]FEI J,CHEN W,HEIDENREICH P,et al.SemanticVoxels:Sequential fusion for 3D pedestrian detection using LiDAR point cloud and semantic segmentation[C]//2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems(MFI).2020:185-190.
[50]QI C R,LIU W,WU C,et al.Frustum pointnets for 3d object detection from rgb-d data[C]//Proceedings of the IEEE Confe-rence on Computer Vision and Pattern Recognition.2018:918-927.
[51]WANG Z,JIA K.Frustum convnet:Slidingfrustums to aggregate local point-wise features for amodal 3d object detection[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS).2019:1742-1749.
[52]PAIGWAR A,SIERRA-GONZALEZ D,ERKENT Ö,et al.Frustum-pointpillars:A multi-stage approach for 3d object detection using rgb camera and lidar[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2021:2926-2933.
[53]SHI S,GUO C,JIANG L,et al.Pv-rcnn:Point-voxel feature set abstraction for 3d object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:10529-10538.
[54]LI J,SUN Y,LUO S,et al.P2V-RCNN:Point to Voxel Feature Learning for 3D Object Detection from Point Clouds[J].IEEE Access,2021,9:98249-98260.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

Review of 3D Target Detection Methods Based on LiDAR Point Clouds

PDF (PC)

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Comments

Recommended 0

[1]	ZHANG Yian, YANG Ying, REN Gang, WANG Gang. Study on Multimodal Online Reviews Helpfulness Prediction Based on Attention Mechanism [J]. Computer Science, 2023, 50(8): 37-44.
[2]	SONG Xinyang, YAN Zhiyuan, SUN Muyi, DAI Linlin, LI Qi, SUN Zhenan. Review of Talking Face Generation [J]. Computer Science, 2023, 50(8): 68-78.
[3]	WANG Xu, WU Yanxia, ZHANG Xue, HONG Ruize, LI Guangsheng. Survey of Rotating Object Detection Research in Computer Vision [J]. Computer Science, 2023, 50(8): 79-92.
[4]	ZHOU Ziyi, XIONG Hailing. Image Captioning Optimization Strategy Based on Deep Learning [J]. Computer Science, 2023, 50(8): 99-110.
[5]	HUO Weile, JING Tao, REN Shuang. Review of 3D Object Detection for Autonomous Driving [J]. Computer Science, 2023, 50(7): 107-118.
[6]	SUN Kaiwei, WANG Zhihao, LIU Hu, RAN Xue. Maximum Overlap Single Target Tracking Algorithm Based on Attention Mechanism [J]. Computer Science, 2023, 50(6A): 220400023-5.
[7]	WANG Xiaotian, LI Bo, KANG Xiaodong, LIU Hanqing, HAN Junling, YANG Jingyi. Study on Phased Target Detection in CT Image [J]. Computer Science, 2023, 50(6A): 220200063-10.
[8]	LIU Hui, TIAN Shuaihua. Study on Improvement of Deep Point Cloud Network Based on Multiple Emphasis Mechanisms [J]. Computer Science, 2023, 50(6A): 220400164-7.
[9]	LIU Jiawei, DU Xin, FAN Fangzhao, XIE Chengbi. Design of Indoor Mapping and Navigation System Based on Multi-sensor [J]. Computer Science, 2023, 50(6A): 220300218-8.
[10]	WU Han, NIE Jiahao, ZHANG Zhaowei, HE Zhiwei, GAO Mingyu. Deep Learning-based Visual Multiple Object Tracking:A Review [J]. Computer Science, 2023, 50(4): 77-87.
[11]	LIU Hang, PU Yuanyuan, LYU Dahua, ZHAO Zhengpeng, XU Dan, QIAN Wenhua. Polarized Self-attention Constrains Color Overflow in Automatic Coloring of Image [J]. Computer Science, 2023, 50(3): 208-215.
[12]	GUO Nan, LI Jingyuan, REN Xi. Survey of Rigid Object Pose Estimation Algorithms Based on Deep Learning [J]. Computer Science, 2023, 50(2): 178-189.
[13]	HE Xionghui, TAN Jiefu, LIU Zhe, XUE Chao, YANG Shaowu, ZHANG Yongjun. Viewpoint-tolerant Scene Recognition Based on Segmentation of Sparse Point Cloud [J]. Computer Science, 2023, 50(1): 87-97.
[14]	MENG Huaru, WU Guowei. Cloth Simulation Filtering Algorithm with Topography Cognition [J]. Computer Science, 2023, 50(1): 156-165.
[15]	YANG Wen-kun, YUAN Xiao-pei, CHEN Xiao-feng, GUO Rui. Spatial Multi-feature Segmentation of 3D Lidar Point Cloud [J]. Computer Science, 2022, 49(8): 143-149.