Fetching the paper…
Reading the bibliography…
3D object detection based on LiDAR-camera fusion is becoming an emerging research theme for autonomous driving.
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 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 , 2019, pp. 1951–1960
1960
Earlier work this paper cites.
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” The International Journal of Robotics Research , vol. 32, no. 11, pp. 1231–1237, 2013
2013
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition . Ieee, 2009, pp. 248–255
2014
Earlier work this paper cites.
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler, and R. Urtasun, “Monocular 3d object detection for autonomous driving,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 2147–2156
2016
Earlier work this paper cites.
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 transactions on pattern analysis and machine intelligence , vol. 40, no. 5, pp. 1259–1272, 2017
2017
Earlier work this paper cites.
C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” in Advances in neural information processing systems , 2017, pp. 5099–5108
2017
Earlier work this paper cites.
B. Xu and Z. Chen, “Multi-level fusion based 3d object detection from monocular images,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 2345–2353
2018
Earlier work this paper cites.
W. Luo, B. Yang, and R. Urtasun, “Fast and furious: Real time end-to-end 3d detection, tracking and motion forecasting with a single convolutional net,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition , 2018, pp. 3569–3577
2018
Earlier work this paper cites.
B. Yang, W. Luo, and R. Urtasun, “Pixor: Real-time 3d object detection from point clouds,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition , 2018, pp. 7652–7660
2018
Earlier work this paper cites.
Y. Zhou and O. Tuzel, “Voxelnet: End-to-end learning for point cloud based 3d object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 4490–4499
2018
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
Earlier work this paper cites.
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 conference on computer vision and pattern recognition , 2018, pp. 918–927
2018
Earlier work this paper cites.
D. Xu, D. Anguelov, and A. Jain, “Pointfusion: Deep sensor fusion for 3d bounding box estimation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 244–253
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) . IEEE, 2018, pp. 1–8
2018
Cited alongside, same era.
M. Liang, B. Yang, S. Wang, and R. Urtasun, “Deep continuous fusion for multi-sensor 3d object detection,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 641–656
2018
Cited alongside, same era.
I. Loshchilov and F. Hutter, “Fixing weight decay regularization in adam,” 2018
2018
Cited alongside, same era.
S. Vora, A. H. Lang, B. Helou, and O. Beijbom, “Pointpainting: Sequential fusion for 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 4604–4612
2020
Closest in time.
Y. Zhou, P. Sun, Y. Zhang, D. Anguelov, J. Gao, T. Ouyang, J. Guo, J. Ngiam, and V. Vasudevan, “End-to-end multi-view fusion for 3d object detection in lidar point clouds,” in Conference on Robot Learning , 2020, pp. 923–932
2020
Closest in time.
L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai, and X. He, “Pi-rcnn: An efficient multi-sensor 3d object detector with point-based attentive cont-conv fusion module.” in AAAI , 2020, pp. 12 460–12 467
2020
Closest in time.
2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. Shi, X. Wang, and H. Li, “Pointrcnn: 3d object proposal generation and detection from point cloud,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 770–779
2019
Cited alongside, same era.
A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, “Pointpillars: Fast encoders for object detection from point clouds,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 12 697–12 705
2019
Cited alongside, same era.
2019
Cited alongside, same era.
M. Liang, B. Yang, Y. Chen, R. Hu, and R. Urtasun, “Multi-task multi-sensor fusion for 3d object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 7345–7353
2019
Cited alongside, same era.
V. A. Sindagi, Y. Zhou, and O. Tuzel, “Mvx-net: Multimodal voxelnet for 3d object detection,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 7276–7282
2019
Cited alongside, same era.
Y. Chen, S. Liu, X. Shen, and J. Jia, “Fast point r-cnn,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 9775–9784
2019
Cited alongside, same era.
S. Shi, Z. Wang, J. Shi, X. Wang, and H. Li, “From points to parts: 3d object detection from point cloud with part-aware and part-aggregation network,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Closest in time.
C. He, H. Zeng, J. Huang, X.-S. Hua, and L. Zhang, “Structure aware single-stage 3d object detection from point cloud,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 873–11 882
2020
Closest in time.
Z. Yang, Y. Sun, S. Liu, and J. Jia, “3dssd: Point-based 3d single stage object detector,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 040–11 048
2020
Closest in time.
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.
I. Radosavovic, R. P. Kosaraju, R. Girshick, K. He, and P. Dollár, “Designing network design spaces,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 10 428–10 436
2020
Closest in time.
2020
Closest in time.
S. Shi, Z. Wang, J. Shi, X. Wang, and H. Li, “From points to parts: 3d object detection from point cloud with part-aware and part-aggregation network,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2020
2020
Closest in time.