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Cited by 1,697Advanced Neural Network ApplicationsAutonomous Vehicle Technology and SafetyAdvanced Vision and Imaging

Joint 3D Proposal Generation and Object Detection from View Aggregation

Open access
Ku, Jason, Mozifian, Melissa, Lee, Jungwook, Harakeh, Ali, Waslander, Steven L.·2018·2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
DOI 10.1109/iros.2018.8594049
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Abstract

We present AVOD, an Aggregate View Object Detection network for autonomous driving scenarios. The proposed neural network architecture uses LIDAR point clouds and RGB images to generate features that are shared by two subnetworks: a region proposal network (RPN) and a second stage detector network. The proposed RPN uses a novel architecture capable of performing multimodal feature fusion on high resolution feature maps to generate reliable 3D object proposals for multiple object classes in road scenes. Using these proposals, the second stage detection network performs accurate oriented 3D bounding box regression and category classification to predict the extents, orientation, and classification of objects in 3D space. Our proposed architecture is shown to produce state of the art results on the KITTI 3D object detection benchmark [1] while running in real time with a low memory footprint, making it a suitable candidate for deployment on autonomous vehicles. Code is available at: https://github.com/kujason/avod.

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Crossref · 10.1109/iros.2018.8594049

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5 of 5 references verified0 citation errors · 0 not checkable

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  • LiDAR-based detection processes 3D point cloud data, offering precise depth estimation and spatial structure representation.

    “In contrast, LiDAR-based detection [73,74,75,76,77] processes 3D point cloud data, offering precise depth estimation and spatial structure representation.”

    From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [75]

    The abstract (p. 1) says AVOD processes LiDAR point clouds alongside RGB images to predict objects’ extents and orientation in 3D space. The feature-map method (p. 3) describes encoding point heights and density, supporting spatial-structure representation. The paper reports accurate 3D localization, but does not establish the claim of precise depth estimation by LiDAR-based detection specifically. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Ku et al. (2017) [103] achieve Pedestrian detection performance on KITTI test set of 50.80% Easy, 42.81% Moderate, and 40.88% Hard.

    “Ku et al. , 2017 [103] | 71.88 % | 81.94 % | 66.38 % | 42.81 % | 50.80 % | 40.88 % | 52.18 % | 64.00 % | 46.61 % | 0.1 s | GPU Titan X (Pascal) |”

    From Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2021) · cites this paper as [103]

  • Ku et al. (2017) [103] achieve Car detection performance on KITTI test set of 81.94% Easy, 71.88% Moderate, and 66.38% Hard.

    “Ku et al. , 2017 [103] | 71.88 % | 81.94 % | 66.38 % | 42.81 % | 50.80 % | 40.88 % | 52.18 % | 64.00 % | 46.61 % | 0.1 s | GPU Titan X (Pascal) |”

    From Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2021) · cites this paper as [103]

  • Ku et al. (2017) [103] achieve Cyclist detection performance on KITTI test set of 64.00% Easy, 52.18% Moderate, and 46.61% Hard.

    “Ku et al. , 2017 [103] | 71.88 % | 81.94 % | 66.38 % | 42.81 % | 50.80 % | 40.88 % | 52.18 % | 64.00 % | 46.61 % | 0.1 s | GPU Titan X (Pascal) |”

    From Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2021) · cites this paper as [103]

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Cites

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Research works printed but not linked: 13

  1. [1][1] A. Geiger, P. Lenz, and R. Urtasun, 'Are we ready for autonomous driving? the kitti vision benchmark suite,' in Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on . IEEE, 2012, pp. 3354-3361. -
  2. [4][4] X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, 'Multi-view 3d object detection network for autonomous driving,' in Computer Vision and Pattern Recognition, 2017. CVPR 2017. IEEE Conference on, . -
  3. [6][6] X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler, and R. Urtasun, '3d object proposals for accurate object class detection,' in NIPS , 2015. -
  4. [8][8] B. Li, '3d fully convolutional network for vehicle detection in point cloud,' in IROS , 2017. -
  5. [9][9] Y. Zhou and O. Tuzel, 'Voxelnet: End-to-end learning for point cloud based 3d object detection,' arXiv preprint arXiv:1711.06396 , 2017. - · arXiv
  6. [10][10] J. Lahoud and B. Ghanem, '2d-driven 3d object detection in rgb-d images,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 4622-4630. -
  7. [12][12] C. R. Qi, H. Su, K. Mo, and L. J. Guibas, 'Pointnet: Deep learning on point sets for 3d classification and segmentation,' arXiv preprint arXiv:1612.00593 , 2016. - · arXiv
  8. [13][13] F. Chabot, M. Chaouch, J. Rabarisoa, C. Teuli` ere, and T. Chateau, 'Deep manta: A coarse-to-fine many-task network for joint 2d and 3d vehicle analysis from monocular image,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017. -
  9. [14][14] A. Mousavian, D. Anguelov, J. Flynn, and J. Kosecka, '3d bounding box estimation using deep learning and geometry,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017. -
  10. [15][15] S. Song and J. Xiao, 'Deep sliding shapes for amodal 3d object detection in rgb-d images,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 808-816. -
  11. [16][16] K. Simonyan and A. Zisserman, 'Very deep convolutional networks for large-scale image recognition,' arXiv preprint arXiv:1409.1556 , 2014. - · arXiv
  12. [17][17] J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara, A. Fathi, I. Fischer, Z. Wojna, Y. Song, S. Guadarrama, and K. Murphy, 'Speed/accuracy trade-offs for modern convolutional object detectors,' in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , July 2017. -
  13. [18][18] F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer, 'Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size,' arXiv preprint arXiv:1602.07360 , 2016. - · arXiv

Cited by

Sorted by citations

Authors
Ku, J., Mozifian, M., Lee, J., Harakeh, A., & Waslander, S. L.
Affiliations
University of Waterloo (CA)
ORCID
Venue
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publisher
IEEE
Published
2018-10
Pages
1-8
ISSN
2153-0858 (ISSN-L)
DOI
10.1109/iros.2018.8594049
OpenAlex
W2963400571
Cited by
1,697
References
5
Citations by year
2026: 70 · 2025: 168 · 2024: 222 · 2023: 299 · 2022: 265 · 2021: 249 · 2020: 244 · 2019: 153 · 2018: 26 · 2017: 1
FWCI
50.64
Type
conference paper
Language
en
Open access
Locations
  • IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
Keywords
3D object detection, autonomous driving, LiDAR point cloud, RGB images, multimodal fusion, region proposal network, 3D bounding box regression, oriented bounding boxes, traffic scenes, real-time detection, KITTI dataset

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