AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home CAAI Artificial Intelligence Research Article
PDF (1.3 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Original Research | Open Access

LWD-3D: Lightweight Detector Based on Self-Attention for 3D Object Detection

Shuo Yang1, Huimin Lu1,2( ), Tohru Kamiya1, Yoshihisa Nakatoh1, Seiichi Serikawa1
School of Engineering, Kyushu Institute of Technology, Fukuoka 804-8550, Japan
School of Information Engineering, Yangzhou University, Yangzhou 225127, China.
Show Author Information

Abstract

Lightweight modules play a key role in 3D object detection tasks for autonomous driving, which are necessary for the application of 3D object detectors. At present, research still focuses on constructing complex models and calculations to improve the detection precision at the expense of the running rate. However, building a lightweight model to learn the global features from point cloud data for 3D object detection is a significant problem. In this paper, we focus on combining convolutional neural networks with self-attention-based vision transformers to realize lightweight and high-speed computing for 3D object detection. We propose light-weight detection 3D (LWD-3D), which is a point cloud conversion and lightweight vision transformer for autonomous driving. LWD-3D utilizes a one-shot regression framework in 2D space and generates a 3D object bounding box from point cloud data, which provides a new feature representation method based on a vision transformer for 3D detection applications. The results of experiment on the KITTI 3D dataset show that LWD-3D achieves real-time detection (time per image < 20 ms). LWD-3D obtains a mean average precision (mAP) 75% higher than that of another 3D real-time detector with half the number of parameters. Our research extends the application of visual transformers to 3D object detection tasks.

Keywords

3D object detectionpoint cloudsvision transformerone-shot regressionreal-time

References

[1]
S. Shi, L. Jiang, J. Deng, Z. Wang, C. Guo, J. Shi, X. Wang, and H. Li, PV-RCNN++: Point-voxel feature set abstraction with local vector representation for 3D object detection, arXiv preprint arXiv: 2102.00463, 2021.
Crossref
[2]
R. Q. Charles, H. Su, M. Kaichun, and L. J. Guibas, PointNet: Deep learning on point sets for 3D classification and segmentation, in Proc. 2017 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 77–85.
Crossref
[3]
A. Geiger, P. Lenz, and R. Urtasun, Are we ready for autonomous driving? The KITTI vision benchmark suite, in Proc. 2012 IEEE Conf. Computer Vision and Pattern Recognition, Providence, RI, USA, 2012, pp. 335–336.
Crossref
[4]
M. Liang, B. Yang, Y. Chen, R. Hu, and R. Urtasun, Multi-task multi-sensor fusion for 3D object detection, in Proc. 2019 IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 7337–7345.
Crossref
[5]
J. Ku, M. Mozifian, J. Lee, A. Harakeh, and S. L. Waslander, Joint 3D proposal generation and object detection from view aggregation, in Proc. 2018 IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), Madrid, Spain, 2018, pp. 1–8.
Crossref
[6]
X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, Multi-view 3D object detection network for autonomous driving, in Proc. 2017 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 6526–6534.
Crossref
[7]
P. Li, X. Chen, and S. Shen, Stereo R-CNN based 3D object detection for autonomous driving, in Proc. 2019 IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 7636–7644.
Crossref
[8]
Y. Zhou and O. Tuzel, VoxelNet: End-to-end learning for point cloud based 3D object detection, in Proc. 2018 IEEE/CVF Conf. Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 4490–4499.
Crossref
[9]
D. Maturana and S. Scherer, VoxNet: A 3D convolutional neural network for real-time object recognition, in Proc. 2015 IEEE/RSJ Int. Conf. Intelligent Robots and Systems, Hamburg, Germany, 2015, pp. 922–928.
Crossref
[10]

X. Chen, K. Kundu, Y. Zhu, H. Ma, S. Fidler, and R. Urtasun, 3D object proposals using stereo imagery for accurate object class detection, IEEE Trans. Pattern Anal. Mach. Intell., vol. 40, no. 5, pp. 1259–1272, 2018.
Crossref Google Scholar
[11]
Y. Chen, S. Liu, X. Shen, and J. Jia, Fast point R-CNN, in Proc. 2019 IEEE/CVF Int. Conf. Computer Vision (ICCV), Seoul, Republic of Korea, 2019, pp. 9774–9783.
Crossref
[12]
S. Shi, X. Wang, and H. Li, PointRCNN: 3D object proposal generation and detection from point cloud, in Proc. 2019 IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 770–779.
Crossref
[13]
W. Zheng, W. Tang, L. Jiang, and C. W. Fu, SE-SSD: Self-ensembling single-stage object detector from point cloud, in Proc. 2021 IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 2021, pp. 14489–14498.
Crossref
[14]
C. R. Qi, Y. Li, H. Su, and L. J. Guibas, PointNet++: Deep hierarchical feature learning on point sets in a metric space, in Proc. 31st Int. Conference on Neural Information Processing Systems, Long Beach, CA, USA, 2017, pp. 5105–5114.
[15]

Y. Yan, Y. Mao, and B. Li, SECOND: Sparsely embedded convolutional detection, Sensors, vol. 18, no. 10, p. 3337, 2018.
Crossref Google Scholar
[16]
C. R. Qi, O. Litany, K. He, and L. Guibas, Deep Hough voting for 3D object detection in point clouds, in Proc. 2019 IEEE/CVF Int. Conf. Computer Vision (ICCV), Seoul, Republic of Korea, 2019, pp. 9276–9285.
[17]
A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, PointPillars: Fast encoders for object detection from point clouds, in Proc. 2019 IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 12689–12697.
Crossref
[18]
M. Simon, S. Milz, K. Amende, and H. M. Gross, Complex-YOLO: Real-time 3D object detection on point clouds, arXiv preprint arXiv: 1803.06199v2, 2018.
Crossref
[19]
Z. Yang, Y. Sun, S. Liu, and J. Jia, 3DSSD: Point-based 3d single stage object detector, in Proc. 2020 IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020, pp. 11037–11045.
Crossref
[20]
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, MobileNets: Efficient convolutional neural networks for mobile vision applications, arXiv preprint arXiv: 1704.04861, 2017.
[21]
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, You only look once: Unified, real-time object detection, in Proc. 2016 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 779–788.
Crossref
[22]
J. Redmon and A. Farhadi. YOLO9000: Better, faster, stronger, in Proc. 2017 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 6517–6525.
Crossref
CAAI Artificial Intelligence Research
Volume 1 Issue 2,
December 2022
Pages 137-143
DOI: 10.26599/AIR.2022.9150009
Cite this article:
Yang S, Lu H, Kamiya T, et al. LWD-3D: Lightweight Detector Based on Self-Attention for 3D Object Detection. CAAI Artificial Intelligence Research, 2022, 1(2): 137-143. https://doi.org/10.26599/AIR.2022.9150009

2918

Views

288

Downloads

1

Crossref

Altmetrics
Received: 05 December 2022
Revised: 01 January 2023
Accepted: 08 January 2023
Published: 10 March 2023
© The author(s) 2022

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Return