Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion

X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, “Multi-view 3d object detection network for autonomous driving,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2017, pp. 1907–1915

1915

Earlier work this paper cites.

Z. Yang, Y. Sun, S. Liu, X. Shen, and J. Jia, “Std: Sparse-to-dense 3d object detector for point cloud,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 1951–1960

1960

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A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2012, pp. 3354–3361

2012

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T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in European conference on computer vision . Springer, 2014, pp. 740–755

2014

Earlier work this paper cites.

S. Song and J. Xiao, “Deep sliding shapes for amodal 3d object detection in rgb-d images,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2016, pp. 808–816

2016

Earlier work this paper cites.

Z. Deng and L. Jan Latecki, “Amodal detection of 3d objects: Inferring 3d bounding boxes from 2d ones in rgb-depth images,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2017, pp. 5762–5770

2017

Earlier work this paper cites.

C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 652–660

2017

Earlier work this paper cites.

K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2961–2969

2017

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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

Original

2017

Earlier work this paper cites.

Y. Yan, Y. Mao, and B. Li, “Second: Sparsely embedded convolutional detection,” Sensors , vol. 18, no. 10, p. 3337, 2018

2018

Cited alongside, same era.

C. R. Qi, W. Liu, C. Wu, H. Su, and L. J. Guibas, “Frustum pointnets for 3d object detection from rgb-d data,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2018, pp. 918–927

2018

Cited alongside, same era.

J. Ku, M. Mozifian, J. Lee, A. Harakeh, and S. L. Waslander, “Joint 3d proposal generation and object detection from view aggregation,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2018, pp. 1–8

2018

Cited alongside, same era.

X. Du, M. H. Ang, S. Karaman, and D. Rus, “A general pipeline for 3d detection of vehicles,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 3194–3200

2018

Cited alongside, same era.

X. Zhao, Z. Liu, R. Hu, and K. Huang, “3d object detection using scale invariant and feature reweighting networks,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 9267–9274

2019

Later among the works it cites.

P. Cao, H. Chen, Y. Zhang, and G. Wang, “Multi-view frustum pointnet for object detection in autonomous driving,” in 2019 IEEE International Conference on Image Processing (ICIP) . IEEE, 2019, pp. 3896–3899

2019

Later among the works it cites.

S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang, and H. Li, “Pv-rcnn: Point-voxel feature set abstraction for 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 10 529–10 538

2020

Closest in time.

Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou, and X. Bai, “Tanet: Robust 3d object detection from point clouds with triple attention.” in AAAI , 2020, pp. 11 677–11 684

2020

Closest in time.