Skip to main content

Advertisement

Springer Nature Link
Log in

A survey of 3D object detection algorithms for intelligent vehicles development

  • Original Article
  • Published:
Artificial Life and Robotics Aims and scope Submit manuscript

Abstract

With the rapid development of Artificial Intelligent algorithms on Computer Vision, 2D object detection has greatly succeeded and been applied in various industrial products. In the past several years, the accuracy of 2D object detection has been dramatically improved, even beyond the human eyes detection ability. However, there is still a limitation of 2D object detection for the applications of Intelligent Driving. A safe and reliable self-driving car needs to detect a 3D model of the around objects so that an intelligent driving car has a perception ability to real driving situations. This paper systematically surveys the development of 3D object detection methods applied to intelligent driving technology. This paper also analyzes the shortcomings of the existing 3D detection algorithms and the future development directions of 3D detection algorithms on intelligent driving.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Ding M, Huo Y, Yi H et al (2020) Learning Depth-Guided Convolutions for Monocular 3D Object Detection. 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp. 11669–11678

  2. Qian R, Garg D, Wang Y et al (2020) End-to-end pseudo-LiDAR for image-based 3D object detection, 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp. 5880–5889

  3. Sun J, Chen L, Xie Y et al (2020) Disp R-CNN: stereo 3D object detection via shape prior guided instance disparity estimation, 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp. 10545–10554

  4. Chen Y, Liu S, Shen X et al (2020) DSGN: deep stereo geometry network for 3D object detection, 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp. 12533–12542

  5. Martin S, Stefan M, Karl A et al (2018) Complex-yolo: real-time 3d objectdetection on point clouds. Computer vision and pattern recognition

  6. Zhen L, Yuren D, Qingqing H et al (2021) A 3D object detection framework for self-driving using YOLOv4, Proceedings of the 8th IIAE international conference on intelligent systems and image processings, pp. 9–15

  7. Alexey B, Chien-Yao W, Hong-Yuan L et al (2020) YOLOv4: optimal speed and accuracy of object detection. Computer vision and pattern recognition

  8. Shi S, Wang X, Li H et al (2019) PointRCNN: 3D object proposal generation and detection from point cloud, IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp. 770–779

  9. Charles R. Q, Hao S, Kaichun M et al (2017) PointNet: deep learning on point sets for 3D classification and segmentation. Computer vision and pattern recognition

  10. Zetong Y, Yanan S, Liu S et al (2019) Std: sparse-to-dense 3d object detector for point cloud, IEEE/CVF conference on computer vision and pattern recognition, pp. 1951–1960

  11. Vora S, Lang A H, Helou B et al (2020) PointPainting: sequential fusion for 3D object detection, 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp. 4603–4611

  12. Liang M, Yang B, Wang S et al (2018) Deep continuous fusion for multi-sensor 3d object detection, Proceedings of the European conference on computer vision (ECCV), pp. 641–656

  13. Chen X, Ma H, Wan J et al (2017) Multi-view 3D object detection network for autonomous driving, 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp. 6526–6534

Download references

Author information

Authors and Affiliations

  1. Kyushu Institute of Technology, Kitakyushu, Japan

    Zhen Li, Miaomiao Zhu, Shi Zhou & Lifeng Zhang

  2. Yangzhou University, Yangzhou, China

    Yuren Du

Authors
  1. Zhen Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Yuren Du
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Miaomiao Zhu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Shi Zhou
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Lifeng Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Zhen Li.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Z., Du, Y., Zhu, M. et al. A survey of 3D object detection algorithms for intelligent vehicles development. Artif Life Robotics 27, 115–122 (2022). https://doi.org/10.1007/s10015-021-00711-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10015-021-00711-0

Keywords