Point Cloud Processing Methods for 3D Point Cloud Detection Tasks

doi:10.12142/ZTECOM.202304005

ZTE Communications ›› 2023, Vol. 21 ›› Issue (4): 38-46.DOI: 10.12142/ZTECOM.202304005

收稿日期:2023-10-07 出版日期:2023-12-07 发布日期:2023-12-07

Point Cloud Processing Methods for 3D Point Cloud Detection Tasks

WANG Chongchong¹, LI Yao², WANG Beibei³, CAO Hong³, ZHANG Yanyong²()

^1.Anhui University, Hefei 230601, China
^2.University of Science and Technology of China, Hefei 230026, China
^3.Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230026, China

Received:2023-10-07 Online:2023-12-07 Published:2023-12-07
About author:WANG Chongchong received his BS degree in computer science and technology from Huazhong Agricultural University, China in 2022. He is currently pursuing a master’s degree in computer science and technology at Anhui University, China.
LI Yao received his BS degree in electronic information engineering from Jilin University, China in 2019. He is currently pursuing a PhD degree in computer science and technology at the University of Science and Technology of China.His research interests include computer vision and intelligent transportation perception system.
WANG Beibei graduated from University of Science and Technology of China with BS in physics in 2014. He furthered his studies at the University of Southern California, USA, where he obtained PhD in physics in 2020 and MS in computer science in parallel. His research interests currently focus on computer vision and multimodal perception methods for autonomous systems.
CAO Hong received his PhD degree from Zhejiang University, China in 2014. He is currently a associate research fellow with the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center (Anhui Artificial Intelligence Laboratory), China. His research interests include autonomous driving, roadside perception and robotics.
ZHANG Yanyong (yanyongz@ustc.edu.cn) received her BS from the University of Science and Technology of China (USTC) in 1997, and PhD from Penn State University in 2002. From 2002 and 2018, she was on the faculty of the Electrical and Computer Engineering Department at Rutgers University, USA. She was also a member of the Wireless Information Networks Laboratory (Winlab). Since July 2018, she joined the school of Computer Science and Technology at USTC. She has 21 years of research experience in the areas of sensor networks, ubiquitous computing, and high-performance computing, and has published more than 140 technical papers in these fields. She received the NSF CAREER award in 2006, and was elevated to IEEE Fellow in 2017. She has served/currently serves as the Associate Editor for several journals, including IEEE/ACM Transactions on Networking, IEEE Transactions on Mobile Computing, IEEE Transactions on Service Computing, IEEE Transactions on Dependable and Secure Computing, and Elsevier Smart Health. She has served on various conference TPCs including DSN, Sensys, Infocom, etc. She is the TPC co-chair of IPSN’22.

摘要/Abstract

Abstract:

Light detection and ranging (LiDAR) sensors play a vital role in acquiring 3D point cloud data and extracting valuable information about objects for tasks such as autonomous driving, robotics, and virtual reality (VR). However, the sparse and disordered nature of the 3D point cloud poses significant challenges to feature extraction. Overcoming limitations is critical for 3D point cloud processing. 3D point cloud object detection is a very challenging and crucial task, in which point cloud processing and feature extraction methods play a crucial role and have a significant impact on subsequent object detection performance. In this overview of outstanding work in object detection from the 3D point cloud, we specifically focus on summarizing methods employed in 3D point cloud processing. We introduce the way point clouds are processed in classical 3D object detection algorithms, and their improvements to solve the problems existing in point cloud processing. Different voxelization methods and point cloud sampling strategies will influence the extracted features, thereby impacting the final detection performance.

Key words: point cloud processing, 3D object detection, point cloud voxelization, bird's eye view, deep learning

. [J]. ZTE Communications, 2023, 21(4): 38-46.

WANG Chongchong, LI Yao, WANG Beibei, CAO Hong, ZHANG Yanyong. Point Cloud Processing Methods for 3D Point Cloud Detection Tasks[J]. ZTE Communications, 2023, 21(4): 38-46.

图/表 10

参考文献 45

1	REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks [J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(6): 1137–1149. DOI: 10.1109/tpami.2016.2577031
2	REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection [EB/OL]. (2016-05-09)[2023-08-20].
3	LAW H, DENG J. CornerNet: detecting objects as paired keypoints [J]. International journal of computer vision, 2020, 128(3): 642–656. DOI: 10.1007/s11263-019-01204-1
4	SHI S S, WANG X G, LI H S. PointRCNN: 3D object proposal generation and detection from point cloud [EB/OL]. (2019-05-16)[2023-08-21].
5	CHEN Y K, LIU J H, ZHANG X Y, et al. VoxelNeXt: fully sparse VoxelNet for 3D object detection and tracking [EB/OL]. (203-03-20)[2023-08-21].
6	YANG Z T, SUN Y N, LIU S, et al. STD: sparse-to-dense 3D object detector for point cloud [EB/OL]. (2019-07-22)[2023-08-21].
7	PHILION J, Lift FIDLER S., splat, shoot: encoding images from arbitrary camera rigs by implicitly unprojecting to 3D [C]//European Conference on Computer Vision. Springer, 2020: 194–210.10.1007/978-3-030-58568-6_12
8	YIN T W, ZHOU X Y, KRÄHENBÜHL P. Center-based 3D object detection and tracking [C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021: 11779–11788. DOI: 10.1109/CVPR46437.2021.01161
9	KLASING K, WOLLHERR D, BUSS M. A clustering method for efficient segmentation of 3D laser data [C]//2008 IEEE International Conference on Robotics and Automation. IEEE, 2008: 4043–4048. DOI: 10.1109/ROBOT.2008.4543832
10	KLASING K, WOLLHERR D, BUSS M. Realtime segmentation of range data using continuous nearest neighbors [C]//2009 IEEE International Conference on Robotics and Automation. IEEE, 2009: 2431–2436. DOI: 10.1109/ROBOT.2009.5152498
11	CHARLES R Q, HAO S, MO K C, et al. PointNet: deep learning on point sets for 3D classification and segmentation [C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017: 77–85. DOI: 10.1109/CVPR.2017.16
12	QI C R, YI L, SU H, et al. PointNet++: deep hierarchical feature learning on point sets in a metric space [EB/OL]. (2017-06-07)[2020-08-21].
13	HUANG J J, HUANG G, ZHU Z, et al. BEVDet: high-performance multi-camera 3D object detection in bird-eye-view [EB/OL]. (2022-06-16)[2023-08-21].
14	LI Z Q, WANG W H, LI H Y, et al. Bevformer: learning bird's-eye-view representation from multi-camera images via spatiotemporal transformers [EB/OL]. (2022-07-13)[2023-08-21].
15	LI Y H, GE Z, YU G Y, et al. BEVDepth: acquisition of reliable depth for multi-view 3D object detection [EB/OL]. (2022-11-30)[2023-08-21].
16	LIU Z J, TANG H T, AMINI A, et al. BEVFusion: Multi-task multi-sensor fusion with unified bird's-eye view representation [EB/OL]. (2022-06-16)[2023-08-21].
17	WANG R H, QIN J, LI K Y, et al. BEV-LaneDet: a simple and effective 3D lane detection baseline [EB/OL]. (203-03-11)[2023-08-21].
18	DONG Y P, KANG C X, ZHANG J L. Benchmarking robustness of 3D object detection to common corruptions in autonomous driving [EB/OL]. (2023-03-20)[2023-08-21].
19	QI C R, LITANY O, HE K M, et al. Deep hough voting for 3D object detection in point clouds [EB/OL]. (2019-08-22)[2023-08-21].
20	HE Y S, SUN W, HUANG H B, et al. PVN 3D: point-wiseadeep 3D keypoints voting network for 6DoF pose estimation [EB/OL]. (2020-03-24)[2023-08-21].
21	ZHOU Y, TUZEL O. VoxelNet: end-to-end learning for point cloud based 3D object detection [C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2018: 4490–4499. DOI: 10.1109/CVPR.2018.00472
22	YAN Y, MAO Y X, LI B. SECOND: sparsely embedded convolutional detection [J]. Sensors, 2018, 18(10): 3337. DOI: 10.3390/s18103337
23	SIMON M, AMENDE K, KRAUS A, et al. Complexer-YOLO: real-time 3D object detection and tracking on semantic point clouds [EB/OL]. (2019-04-16)[2023-08-21].
24	LANG A H, VORA S, CAESAR H, et al. PointPillars: fast encoders for object detection from point clouds [EB/OL]. (2020-03-24)[2023-08-21].
25	REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks [J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(6): 1137–1149. DOI: 10.1109/TPAMI.2016.2577031
26	CAESAR H, BANKITI V, LANG A H, et al. nuScenes: a multimodal dataset for autonomous driving [EB/OL]. (2020-05-05)[2023-08-21].
27	YIN T W, ZHOU X Y, KRÄHENBÜHL P. Center-based 3D object detection and tracking [EB/OL]. (2021-01-06)[2023-08-21].
28	FAN L, WANG F, WANG N Y, et al. Fully sparse 3D object detection [EB/OL]. (2022-10-03)[2023-08-21].
29	ZHOU C, ZHANG Y N, CHEN J X, et al. OcTr: octree-based transformer for 3D object detection [EB/OL]. (2023-03-22)[2023-08-21].
30	SUN P, KRETZSCHMAR H, DOTIWALLA X, et al. Scalability in perception for autonomous driving: waymo open dataset [EB/OL]. (2023-03-22)[2023-08-21].
31	GEIGER A, LENZ P, URTASUN R, et al. Are we ready for autonomous driving? The KITTI vision benchmark suite [C]//2012 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2012. DOI: 10.1109/CVPR.2012.6248074
32	YANG Z T, SUN Y N, LIU S, et al. 3DSSD: point-based 3D single stage object detector [EB/OL]. (2020-02-24)[2023-08-21].
33	SHI S S, WANG X G, LI H S. PointRCNN: 3D object proposal generation and detection from point cloud [C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020: 770–779. DOI: 10.1109/CVPR.2019.00086
34	QI C R, LIU W, WU C X, et al. Frustum PointNets for 3D object detection from RGB-D data [EB/OL]. (2018-04-13)[2023-08-21].
35	CHEN Y L, LIU S, SHEN X Y, et al. Fast point R-CNN [EB/OL]. (2019-08-16)[2023-08-21].
36	YANG Z T, SUN Y N, LIU S, et al. 3DSSD: point-based 3D single stage object detector [C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020: 11037–11045. DOI: 10.1109/CVPR42600.2020.01105
37	YANG Z T, SUN Y N, LIU S, et al. IPOD: intensive point-based object detector for point cloud [EB/OL]. (2018-12-13)[2023-08-21].
38	YANG Z T, SUN Y N, LIU S, et al. STD: sparse-to-dense 3D object detector for point cloud [C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2020: 1951–1960. DOI: 10.1109/ICCV.2019.00204
39	SHI S S, GUO C X, JIANG L, et al. PV-RCNN: point-voxel feature set abstraction for 3D object detection [EB/OL]. (2021-04-09)[2023-08-21].
40	YOU Y R, WANG Y, CHAO W-L, et al. Pseudo-lidar++: accurate depth for 3d object detection in autonomous driving [EB/OL]. (2020-02-15)[2023-08-21].
41	HE C H, ZENG H, HUANG J Q, et al. Structure aware single-stage 3D object detection from point cloud [C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020: 11870–11879. DOI: 10.1109/CVPR42600.2020.01189
42	SHI W J. Point-GNN: graph neural network for 3D object detection in a point cloud [EB/OL]. (2020-03-02)[2023-08-21].
43	SCARSELLI F, GORI M, TSOI A C, et al. The graph neural network model [J]. IEEE transactions on neural networks, 2009, 20(1): 61–80. DOI: 10.1109/tnn.2008.2005605
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). ACM, 2018: 1–8. DOI: 10.1109/IROS.2018.8594049
45	LIANG M, YANG B, CHEN Y, et al. Multi-task multi-sensor fusion for 3D object detection [C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020: 7337–7345. DOI: 10.1109/CVPR.2019.00752

Method	Modality	AP_Car			AP_Pedestrian			AP_Cyclist
Method	Modality	Easy	Moderate	Hard	Easy	Moderate	Hard	Easy	Moderate	Hard
VoxelNet^[21]	LiDAR	81.97	65.46	62.85	57.86	53.42	48.87	67.17	47.65	45.11
SECOND^[22]	LiDAR	83.13	73.66	66.20	51.07	42.56	37.29	70.51	53.85	46.90
PointPillars^[24]	LiDAR	79.05	74.99	68.30	52.08	43.53	41.49	75.78	59.07	52.92
OcTr^[29]	LiDAR	88.43	78.57	77.16	61.49	57.17	52.35	85.29	70.44	66.17

Method	Modality	AP_Car			AP_Pedestrian			AP_Cyclist
Method	Modality	Easy	Moderate	Hard	Easy	Moderate	Hard	Easy	Moderate	Hard
VoxelNet^[21]	LiDAR	81.97	65.46	62.85	57.86	53.42	48.87	67.17	47.65	45.11
SECOND^[22]	LiDAR	83.13	73.66	66.20	51.07	42.56	37.29	70.51	53.85	46.90
PointPillars^[24]	LiDAR	79.05	74.99	68.30	52.08	43.53	41.49	75.78	59.07	52.92
OcTr^[29]	LiDAR	88.43	78.57	77.16	61.49	57.17	52.35	85.29	70.44	66.17

Method	Recall_{4 096}	Recall_{1 024}	Recall₅₁₂
D-FPS	99.7%	65.9%	51.8%
F-FPS, λ = 0.0	99.7%	83.5%	68.4%
F-FPS, λ = 0.5	99.7%	84.9%	74.9%
F-FPS, λ = 1.0	99.7%	89.2%	76.1%
F-FPS, λ = 2.0	99.7%	86.3%	73.7%

Method	Recall_{4 096}	Recall_{1 024}	Recall₅₁₂
D-FPS	99.7%	65.9%	51.8%
F-FPS, λ = 0.0	99.7%	83.5%	68.4%
F-FPS, λ = 0.5	99.7%	84.9%	74.9%
F-FPS, λ = 1.0	99.7%	89.2%	76.1%
F-FPS, λ = 2.0	99.7%	86.3%	73.7%

Method	AP_{Car‐3D Detection}			AP_{C ar‐BEV Detection}			AP_{Cyclist‐3D Detection}			AP_{Cyclist‐BEV Detection}
Method	Easy	Moderate	Hard	Easy	Moderate	Hard	Easy	Moderate	Hard	Easy	Moderate	Hard
SECOND^[22]	83.34	72.55	65.82	89.39	83.77	78.59	71.33	52.08	45.83	76.50	56.05	49.45
Fast Point R-CNN^[35]	85.29	77.40	70.24	90.87	87.84	80.52	-	-	-	-	-	-
STD^[38]	87.95	79.71	75.09	94.74	89.19	86.42	78.69	61.59	55.30	81.36	67.23	59.35
PV-RCNN^[39]	90.25	81.43	76.82	94.98	90.65	86.14	78.60	63.71	57.65	82.49	68.89	62.41

Point Cloud Processing Methods for 3D Point Cloud Detection Tasks

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 45

相关文章 0

编辑推荐

Metrics