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In the realm of modern autonomous driving, the perception system is indispensable for accurately assessing the state of the surrounding environment, thereby enabling informed prediction and planning.
Y. Xiang, W. Choi, Y. Lin, and S. Savarese, “Data-driven 3d voxel patterns for object category recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 1903–1911
1911
Earlier work this paper cites.
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.
P. Li, S. Su, and H. Zhao, “Rts3d: Real-time stereo 3d detection from 4d feature-consistency embedding space for autonomous driving,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 3, 2021, pp. 1930–1939
1939
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/CVF international conference on computer vision , 2019, pp. 1951–1960
1960
Earlier work this paper cites.
A. Simonelli, S. R. Bulo, L. Porzi, M. López-Antequera, and P. Kontschieder, “Disentangling monocular 3d object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 1991–1999
1999
Earlier work this paper cites.
A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” in 2012 IEEE conference on computer vision and pattern recognition . IEEE, 2012, pp. 3354–3361
2012
Earlier work this paper cites.
M. Zeeshan Zia, M. Stark, and K. Schindler, “Are cars just 3d boxes?-jointly estimating the 3d shape of multiple objects,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2014, pp. 3678–3685
2014
Earlier work this paper cites.
B. Graham, “Spatially-sparse convolutional neural networks,” arXiv preprint arXiv:1409.6070 , 2014
2014
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention . Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
B. Cai, X. Xu, K. Jia, C. Qing, and D. Tao, “Dehazenet: An end-to-end system for single image haze removal,” IEEE transactions on image processing , vol. 25, no. 11, pp. 5187–5198, 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , Jun 2016
2016
Earlier work this paper cites.
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, pp. 7074–7082
2017
Earlier work this paper cites.
A. Kendall, H. Martirosyan, S. Dasgupta, P. Henry, R. Kennedy, A. Bachrach, and A. Bry, “End-to-end learning of geometry and context for deep stereo regression,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 66–75
2017
Earlier work this paper cites.
C. Godard, O. Mac Aodha, and G. J. Brostow, “Unsupervised monocular depth estimation with left-right consistency,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 270–279
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence , p. 1137–1149, Jun 2017
2017
Earlier work this paper cites.
T.-Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , Jul 2017
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.
C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
Z. Wang, W. Zhan, and M. Tomizuka, “Fusing bird view lidar point cloud and front view camera image for deep object detection,” Cornell University - arXiv,Cornell University - arXiv , Nov 2017
2017
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.
2018
Earlier work this paper cites.
C. Sakaridis, D. Dai, and L. Van Gool, “Semantic foggy scene understanding with synthetic data,” International Journal of Computer Vision , vol. 126, pp. 973–992, 2018
2018
Earlier work this paper cites.
A. Kundu, Y. Li, and J. M. Rehg, “3d-rcnn: Instance-level 3d object reconstruction via render-and-compare,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 3559–3568
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.
B. Yang, M. Liang, and R. Urtasun, “Hdnet: Exploiting hd maps for 3d object detection,” in Conference on Robot Learning . PMLR, 2018, pp. 146–155
2018
Earlier work this paper cites.
J. Beltrán, C. Guindel, F. M. Moreno, D. Cruzado, F. Garcia, and A. De La Escalera, “Birdnet: a 3d object detection framework from lidar information,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2018, pp. 3517–3523
2018
Earlier work this paper cites.
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
Earlier work this paper cites.
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
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.
2018
Earlier work this paper cites.
B. Graham, M. Engelcke, and L. v. d. Maaten, “3d semantic segmentation with submanifold sparse convolutional networks,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , Jun 2018
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
Earlier work this paper cites.
E. Arnold, O. Y. Al-Jarrah, M. Dianati, S. Fallah, D. Oxtoby, and A. Mouzakitis, “A survey on 3d object detection methods for autonomous driving applications,” IEEE Transactions on Intelligent Transportation Systems , vol. 20, no. 10, pp. 3782–3795, 2019
2019
Earlier work this paper cites.
Y. Wang, W.-L. Chao, D. Garg, B. Hariharan, M. Campbell, and K. Q. Weinberger, “Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 8445–8453
2019
Earlier work this paper cites.
G. Brazil and X. Liu, “M3d-rpn: Monocular 3d region proposal network for object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 9287–9296
2019
Earlier work this paper cites.
L. Liu, J. Lu, C. Xu, Q. Tian, and J. Zhou, “Deep fitting degree scoring network for monocular 3d object detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 1057–1066
2019
Earlier work this paper cites.
X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang, and X. Fan, “Accurate monocular 3d object detection via color-embedded 3d reconstruction for autonomous driving,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 6851–6860
2019
Earlier work this paper cites.
J. Ku, A. D. Pon, and S. L. Waslander, “Monocular 3d object detection leveraging accurate proposals and shape reconstruction,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 11 867–11 876
2019
Earlier work this paper cites.
S. Shi, X. Wang, and H. Li, “Pointrcnn: 3d object proposal generation and detection from point cloud,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 770–779
2019
Earlier work this paper cites.
Z. Liu, H. Tang, Y. Lin, and S. Han, “Point-voxel cnn for efficient 3d deep learning,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Earlier work this paper cites.
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/CVF conference on computer vision and pattern recognition , 2019, pp. 12 697–12 705
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
Z. Wang and K. Jia, “Frustum convnet: Sliding frustums to aggregate local point-wise features for amodal 3d object detection,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2019, pp. 1742–1749
2019
Earlier work this paper cites.
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,” Conference on Robot Learning,Conference on Robot Learning , Jan 2019
2019
Earlier work this paper cites.
X. Huang, P. Wang, X. Cheng, D. Zhou, Q. Geng, and R. Yang, “The apolloscape open dataset for autonomous driving and its application,” IEEE transactions on pattern analysis and machine intelligence , vol. 42, no. 10, pp. 2702–2719, 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
D. Liu, R. Yu, and H. Su, “Extending adversarial attacks and defenses to deep 3d point cloud classifiers,” in 2019 IEEE International Conference on Image Processing (ICIP) . IEEE, 2019, pp. 2279–2283
2019
Earlier work this paper cites.
R. Kesten, M. Usman, J. Houston, T. Pandya, K. Nadhamuni, A. Ferreira, M. Yuan, B. Low, A. Jain, P. Ondruska et al. , “Lyft level 5 av dataset 2019,” urlhttps://level5. lyft. com/dataset , vol. 1, p. 3, 2019
2019
Earlier work this paper cites.
A. Patil, S. Malla, H. Gang, and Y.-T. Chen, “The h3d dataset for full-surround 3d multi-object detection and tracking in crowded urban scenes,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 9552–9557
2019
Earlier work this paper cites.
M.-F. Chang, J. Lambert, P. Sangkloy, J. Singh, S. Bak, A. Hartnett, D. Wang, P. Carr, S. Lucey, D. Ramanan et al. , “Argoverse: 3d tracking and forecasting with rich maps,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 8748–8757
2019
Earlier work this paper cites.
T. He and S. Soatto, “Mono3d++: Monocular 3d vehicle detection with two-scale 3d hypotheses and task priors,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, no. 01, 2019, pp. 8409–8416
2019
Earlier work this paper cites.
A. Naiden, V. Paunescu, G. Kim, B. Jeon, and M. Leordeanu, “Shift r-cnn: Deep monocular 3d object detection with closed-form geometric constraints,” in 2019 IEEE international conference on image processing (ICIP) . IEEE, 2019, pp. 61–65
2019
Earlier work this paper cites.
B. Li, W. Ouyang, L. Sheng, X. Zeng, and X. Wang, “Gs3d: An efficient 3d object detection framework for autonomous driving,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 1019–1028
2019
Earlier work this paper cites.
Z. Qin, J. Wang, and Y. Lu, “Monogrnet: A geometric reasoning network for monocular 3d object localization,” in Proceedings of the AAAI conference on artificial intelligence , vol. 33, no. 01, 2019, pp. 8851–8858
2019
Earlier work this paper cites.
W. Bao, B. Xu, and Z. Chen, “Monofenet: Monocular 3d object detection with feature enhancement networks,” IEEE Transactions on Image Processing , vol. 29, pp. 2753–2765, 2019
2019
Earlier work this paper cites.
J. Chang and G. Wetzstein, “Deep optics for monocular depth estimation and 3d object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 10 193–10 202
2019
Earlier work this paper cites.
Z. Qin, J. Wang, and Y. Lu, “Triangulation learning network: from monocular to stereo 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 7615–7623
2019
Earlier work this paper cites.
H. Königshof, N. O. Salscheider, and C. Stiller, “Realtime 3d object detection for automated driving using stereo vision and semantic information,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC) . IEEE, 2019, pp. 1405–1410
2019
Earlier work this paper cites.
P. Li, X. Chen, and S. Shen, “Stereo r-cnn based 3d object detection for autonomous driving,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 7644–7652
2019
Earlier work this paper cites.
J. Zarzar, S. Giancola, and B. Ghanem, “Pointrgcn: Graph convolution networks for 3d vehicles detection refinement,” arXiv: Computer Vision and Pattern Recognition,arXiv: Computer Vision and Pattern Recognition , Nov 2019
2019
Earlier work this paper cites.
J. Ngiam, B. Caine, W. Han, B. Yang, Y. Chai, P. Sun, Y. Zhou, X. Yi, O. Alsharif, P. Nguyen, Z. Chen, J. Shlens, and V. Vasudevan, “Starnet: Targeted computation for object detection in point clouds,” Cornell University - arXiv,Cornell University - arXiv , Aug 2019
2019
Earlier work this paper cites.
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
Earlier work this paper cites.
K. Shin, Y. P. Kwon, and M. Tomizuka, “Roarnet: A robust 3d object detection based on region approximation refinement,” in 2019 IEEE intelligent vehicles symposium (IV) . IEEE, 2019, pp. 2510–2515
2019
Earlier work this paper cites.
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz, and H. Michael Gross, “Complexer-yolo: Real-time 3d object detection and tracking on semantic point clouds,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , 2019, pp. 0–0
2019
Earlier work this paper cites.
G. P. Meyer, J. Charland, D. Hegde, A. Laddha, and C. Vallespi-Gonzalez, “Sensor fusion for joint 3d object detection and semantic segmentation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops , 2019, pp. 0–0
2019
Earlier work this paper cites.
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/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 7345–7353
2019
Earlier work this paper cites.
J. Ku, A. Pon, and S. Waslander, “Monocular 3d object detection leveraging accurate proposals and shape reconstruction,” Cornell University - arXiv,Cornell University - arXiv , Apr 2019
2019
Earlier work this paper cites.
J. J. Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove, “Deepsdf: Learning continuous signed distance functions for shape representation,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , Jun 2019
2019
Earlier work this paper cites.
E. Jörgensen, C. Zach, and F. Kahl, “Monocular 3d object detection and box fitting trained end-to-end using intersection-over-union loss.” Cornell University - arXiv,Cornell University - arXiv , Jun 2019
2019
Earlier work this paper cites.
H.-N. Hu, Q.-Z. Cai, D. Wang, J. Lin, M. Sun, P. Krahenbuhl, T. Darrell, and F. Yu, “Joint monocular 3d vehicle detection and tracking,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 5390–5399
2019
Earlier work this paper cites.
X. Weng and K. Kitani, “Monocular 3d object detection with pseudo-lidar point cloud,” in Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops , 2019, pp. 0–0
2019
Earlier work this paper cites.
X. Guo, K. Yang, W. Yang, X. Wang, and H. Li, “Group-wise correlation stereo network,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 3273–3282
2019
Earlier work this paper 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
Earlier work this paper cites.
X. Zhou, D. Wang, and P. Krähenbühl, “Objects as points,” arXiv: Computer Vision and Pattern Recognition,arXiv: Computer Vision and Pattern Recognition , Apr 2019
2019
Earlier work this paper cites.
L. N. Smith and N. Topin, “Super-convergence: Very fast training of neural networks using large learning rates,” in Artificial intelligence and machine learning for multi-domain operations applications , vol. 11006. SPIE, 2019, pp. 369–386
2019
Earlier work this paper cites.
G. P. Meyer, A. Laddha, E. Kee, C. Vallespi-Gonzalez, and C. K. Wellington, “Lasernet: An efficient probabilistic 3d object detector for autonomous driving,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 12 677–12 686
2019
Earlier work this paper cites.
Y. Chen, S. Liu, X. Shen, and J. Jia, “Fast point r-cnn,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 9775–9784
2019
Earlier work this paper cites.
P. Li, H. Zhao, P. Liu, and F. Cao, “Rtm3d: Real-time monocular 3d detection from object keypoints for autonomous driving,” in European Conference on Computer Vision . Springer, 2020, pp. 644–660
2020
Earlier work this paper cites.
Y. Cai, B. Li, Z. Jiao, H. Li, X. Zeng, and X. Wang, “Monocular 3d object detection with decoupled structured polygon estimation and height-guided depth estimation,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 07, 2020, pp. 10 478–10 485
2020
Earlier work this paper cites.
Y. Chen, L. Tai, K. Sun, and M. Li, “Monopair: Monocular 3d object detection using pairwise spatial relationships,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 12 093–12 102
2020
Earlier work this paper cites.
Y. Chen, S. Liu, X. Shen, and J. Jia, “Dsgn: Deep stereo geometry network for 3d object detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 12 536–12 545
2020
Earlier work this paper cites.
X. Shi, Z. Chen, and T.-K. Kim, “Distance-normalized unified representation for monocular 3d object detection,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX 16 . Springer, 2020, pp. 91–107
2020
Earlier work this paper cites.
G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen, and D. Cao, “Centernet3d: An anchor free object detector for autonomous driving.” Cornell University - arXiv,Cornell University - arXiv , Jul 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
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
Earlier work this paper cites.
J. H. Yoo, Y. Kim, J. Kim, and J. W. Choi, “3d-cvf: Generating joint camera and lidar features using cross-view spatial feature fusion for 3d object detection,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXVII 16 . Springer, 2020, pp. 720–736
2020
Earlier work this paper cites.
W. Shi and R. Rajkumar, “Point-gnn: Graph neural network for 3d object detection in a point cloud,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 1711–1719
2020
Earlier work this paper cites.
H. Kuang, B. Wang, J. An, M. Zhang, and Z. Zhang, “Voxel-fpn: Multi-scale voxel feature aggregation for 3d object detection from lidar point clouds,” Sensors , vol. 20, no. 3, p. 704, 2020
2020
Earlier work this paper cites.
L. Du, X. Ye, X. Tan, J. Feng, Z. Xu, E. Ding, and S. Wen, “Associate-3ddet: Perceptual-to-conceptual association for 3d point cloud object detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 13 329–13 338
2020
Earlier work this paper cites.
M. Ye, S. Xu, and T. Cao, “Hvnet: Hybrid voxel network for lidar based 3d object detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 1631–1640
2020
Earlier work this paper cites.
Q. Chen, L. Sun, E. Cheung, and A. L. Yuille, “Every view counts: Cross-view consistency in 3d object detection with hybrid-cylindrical-spherical voxelization,” Advances in Neural Information Processing Systems , vol. 33, pp. 21 224–21 235, 2020
2020
Earlier work this paper cites.
T. Wang, X. Zhu, and D. Lin, “Reconfigurable voxels: A new representation for lidar-based point clouds,” arXiv: Computer Vision and Pattern Recognition,arXiv: Computer Vision and Pattern Recognition , Apr 2020
2020
Earlier work this paper cites.
X. Zhu, Y. Ma, T. Wang, Y. Xu, J. Shi, and D. Lin, SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds , Jan 2020, p. 581–597
2020
Earlier work this paper cites.
J. Wang, S. Lan, M. Gao, and L. S. Davis, “Infofocus: 3d object detection for autonomous driving with dynamic information modeling,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part X 16 . Springer, 2020, pp. 405–420
2020
Earlier work this paper cites.
A. Bewley, P. Sun, T. Mensink, D. Anguelov, and C. Sminchisescu, “Range conditioned dilated convolutions for scale invariant 3d object detection,” Conference on Robot Learning,Conference on Robot Learning , May 2020
2020
Earlier work this paper cites.
H. Tang, Z. Liu, S. Zhao, Y. Lin, J. Lin, H. Wang, and S. Han, “Searching efficient 3d architectures with sparse point-voxel convolution,” in European conference on computer vision . Springer, 2020, pp. 685–702
2020
Earlier work this paper cites.
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
Earlier work this paper cites.
T. Huang, Z. Liu, X. Chen, and X. Bai, “Epnet: Enhancing point features with image semantics for 3d object detection,” in European Conference on Computer Vision . Springer, 2020, pp. 35–52
2020
Earlier work this paper cites.
Y. Wu, Y. Wang, S. Zhang, and H. Ogai, “Deep 3d object detection networks using lidar data: A review,” IEEE Sensors Journal , vol. 21, no. 2, pp. 1152–1171, 2020
2020
Earlier work this paper cites.
H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 621–11 631
2020
Earlier work this paper cites.
P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V. Patnaik, P. Tsui, J. Guo, Y. Zhou, Y. Chai, B. Caine et al. , “Scalability in perception for autonomous driving: Waymo open dataset,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 2446–2454
2020
Earlier work this paper cites.
J. Sun, Y. Cao, Q. A. Chen, and Z. M. Mao, “Towards robust { \{ LiDAR-based } \} perception in autonomous driving: General black-box adversarial sensor attack and countermeasures,” in 29th USENIX Security Symposium (USENIX Security 20) , 2020, pp. 877–894
2020
Earlier work this paper cites.
Q.-H. Pham, P. Sevestre, R. S. Pahwa, H. Zhan, C. H. Pang, Y. Chen, A. Mustafa, V. Chandrasekhar, and J. Lin, “A 3d dataset: Towards autonomous driving in challenging environments,” in 2020 IEEE International conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 2267–2273
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
D. Beker, H. Kato, M. A. Morariu, T. Ando, T. Matsuoka, W. Kehl, and A. Gaidon, “Monocular differentiable rendering for self-supervised 3d object detection,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXI 16 . Springer, 2020, pp. 514–529
2020
Earlier work this paper cites.
S. Zakharov, W. Kehl, A. Bhargava, and A. Gaidon, “Autolabeling 3d objects with differentiable rendering of sdf shape priors,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 12 224–12 233
2020
Earlier work this paper cites.
Z. Liu, Z. Wu, and R. Tóth, “Smoke: Single-stage monocular 3d object detection via keypoint estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , 2020, pp. 996–997
2020
Earlier work this paper cites.
G. Brazil, G. Pons-Moll, X. Liu, and B. Schiele, “Kinematic 3d object detection in monocular video,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIII 16 . Springer, 2020, pp. 135–152
2020
Earlier work this paper cites.
A. Simonelli, S. R. Bulo, L. Porzi, E. Ricci, and P. Kontschieder, “Towards generalization across depth for monocular 3d object detection,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXII 16 . Springer, 2020, pp. 767–782
2020
Earlier work this paper cites.
X. Ma, S. Liu, Z. Xia, H. Zhang, X. Zeng, and W. Ouyang, Rethinking Pseudo-LiDAR Representation , Jan 2020, p. 311–327
2020
Earlier work this paper cites.
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu, and P. Luo, “Learning depth-guided convolutions for monocular 3d object detection,” in Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition workshops , 2020, pp. 1000–1001
2020
Earlier work this paper cites.
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou, and H. Bao, “Disp r-cnn: Stereo 3d object detection via shape prior guided instance disparity estimation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 10 548–10 557
2020
Earlier work this paper cites.
Z. Xu, W. Zhang, X. Ye, X. Tan, W. Yang, S. Wen, E. Ding, A. Meng, and L. Huang, “Zoomnet: Part-aware adaptive zooming neural network for 3d object detection,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 07, 2020, pp. 12 557–12 564
2020
Earlier work this paper cites.
W. Peng, H. Pan, H. Liu, and Y. Sun, “Ida-3d: Instance-depth-aware 3d object detection from stereo vision for autonomous driving,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 13 015–13 024
2020
Earlier work this paper cites.
Y. You, Y. Wang, W.-L. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell, and K. Q. Weinberger, “Pseudo-lidar++: Accurate depth for 3d object detection in autonomous driving,” in ICLR , 2020
2020
Earlier work this paper cites.
R. Qian, D. Garg, Y. Wang, Y. You, S. Belongie, B. Hariharan, M. Campbell, K. Q. Weinberger, and W.-L. Chao, “End-to-end pseudo-lidar for image-based 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 5881–5890
2020
Earlier work this paper cites.
C. Li, J. Ku, and S. L. Waslander, “Confidence guided stereo 3d object detection with split depth estimation,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 5776–5783
2020
Earlier work this paper cites.
H. Königshof and C. Stiller, “Learning-based shape estimation with grid map patches for realtime 3d object detection for automated driving,” in 2020 IEEE 23rd International conference on intelligent transportation systems (ITSC) . IEEE, 2020, pp. 1–6
2020
Earlier work this paper cites.
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Q. Weinberger, and W.-L. Chao, “Wasserstein distances for stereo disparity estimation,” Advances in Neural Information Processing Systems , vol. 33, pp. 22 517–22 529, 2020
2020
Earlier work this paper cites.
X. Cheng, Y. Zhong, M. Harandi, Y. Dai, X. Chang, H. Li, T. Drummond, and Z. Ge, “Hierarchical neural architecture search for deep stereo matching,” Advances in neural information processing systems , vol. 33, pp. 22 158–22 169, 2020
2020
Earlier work this paper cites.
Y. Zhang, Y. Chen, X. Bai, S. Yu, K. Yu, Z. Li, and K. Yang, “Adaptive unimodal cost volume filtering for deep stereo matching,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 07, 2020, pp. 12 926–12 934
2020
Earlier work this paper cites.
J. Philion and S. Fidler, “Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIV 16 . Springer, 2020, pp. 194–210
2020
Earlier work this paper cites.
A. D. Pon, J. Ku, C. Li, and S. L. Waslander, “Object-centric stereo matching for 3d object detection,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 8383–8389
2020
Earlier work this paper cites.
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 Proceedings of the AAAI conference on artificial intelligence , vol. 34, no. 07, 2020, pp. 12 460–12 467
2020
Earlier work this paper cites.
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 , vol. 43, no. 8, pp. 2647–2664, 2020
2020
Earlier work this paper cites.
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 Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 07, 2020, pp. 11 677–11 684
2020
Earlier work this paper cites.
H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li, and G. Wang, “Segvoxelnet: Exploring semantic context and depth-aware features for 3d vehicle detection from point cloud,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) , May 2020
2020
Earlier work this paper cites.
Q. Chen, L. Sun, Z. Wang, K. Jia, and A. Yuille, “Object as hotspots: An anchor-free 3d object detection approach via firing of hotspots,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXI 16 . Springer, 2020, pp. 68–84
2020
Earlier work this paper 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
Earlier work this paper cites.
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
Earlier work this paper cites.
S. Pang, D. Morris, and H. Radha, “Clocs: Camera-lidar object candidates fusion for 3d object detection,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 10 386–10 393
2020
Earlier work this paper cites.
X. Wang, W. Yin, T. Kong, Y. Jiang, L. Li, and C. Shen, “Task-aware monocular depth estimation for 3d object detection,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 07, 2020, pp. 12 257–12 264
2020
Earlier work this paper cites.
X. Ye, L. Du, Y. Shi, Y. Li, X. Tan, J. Feng, E. Ding, and S. Wen, “Monocular 3d object detection via feature domain adaptation,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part IX 16 . Springer, 2020, pp. 17–34
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in European conference on computer vision . Springer, 2020, pp. 213–229
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
G. P. Meyer, J. Charland, S. Pandey, A. Laddha, S. Gautam, C. Vallespi-Gonzalez, and C. K. Wellington, “Laserflow: Efficient and probabilistic object detection and motion forecasting,” IEEE Robotics and Automation Letters , vol. 6, no. 2, pp. 526–533, 2020
2020
Cited alongside, same era.
A. Barrera, C. Guindel, J. Beltrán, and F. García, “Birdnet+: End-to-end 3d object detection in lidar bird’s eye view,” in 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2020, pp. 1–6
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Y. Zhang, J. Lu, and J. Zhou, “Objects are different: Flexible monocular 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 3289–3298
2021
Y. Li, Z. Ge, G. Yu, J. Yang, Z. Wang, Y. Shi, J. Sun, and Z. Li, “Bevdepth: Acquisition of reliable depth for multi-view 3d object detection,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 2, 2023, pp. 1477–1485
2023
Later among the works it cites.
L. Yang, X. Zhang, J. Li, L. Wang, M. Zhu, C. Zhang, and H. Liu, “Mix-teaching: A simple, unified and effective semi-supervised learning framework for monocular 3d object detection,” IEEE Transactions on Circuits and Systems for Video Technology , 2023
2023
Later among the works it cites.
Y. Liu, J. Yan, F. Jia, S. Li, A. Gao, T. Wang, and X. Zhang, “Petrv2: A unified framework for 3d perception from multi-camera images,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 3262–3272
2023
Later among the works it cites.
H. Liu, Y. Teng, T. Lu, H. Wang, and L. Wang, “Sparsebev: High-performance sparse 3d object detection from multi-camera videos,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 18 580–18 590
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Cited alongside, same era.
Y. Liu, L. Wang, and M. Liu, “Yolostereo3d: A step back to 2d for efficient stereo 3d detection,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 13 018–13 024
2021
Cited alongside, same era.
C. Reading, A. Harakeh, J. Chae, and S. L. Waslander, “Categorical depth distribution network for monocular 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 8555–8564
2021
Cited alongside, same era.
T. Wang, X. Zhu, J. Pang, and D. Lin, “Fcos3d: Fully convolutional one-stage monocular 3d object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 913–922
2021
Cited alongside, same era.
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan, and W. Ouyang, “Geometry uncertainty projection network for monocular 3d object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 3111–3121
2021
Cited alongside, same era.
D. Park, R. Ambrus, V. Guizilini, J. Li, and A. Gaidon, “Is pseudo-lidar needed for monocular 3d object detection?” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 3142–3152
2021
Cited alongside, same era.
J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang, and H. Li, “Voxel r-cnn: Towards high performance voxel-based 3d object detection,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 2, 2021, pp. 1201–1209
2021
Cited alongside, same era.
T. Yin, X. Zhou, and P. Krahenbuhl, “Center-based 3d object detection and tracking.” in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , Jun 2021
2021
Cited alongside, same era.
J. Mao, Y. Xue, M. Niu, H. Bai, J. Feng, X. Liang, H. Xu, and C. Xu, “Voxel transformer for 3d object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 3164–3173
2021
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
Z. Song, H. Wei, C. Jia, Y. Xia, X. Li, and C. Zhang, “Vp-net: Voxels as points for 3d object detection,” IEEE Transactions on Geoscience and Remote Sensing , 2023
2023
Later among the works it cites.
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,” International Journal of Computer Vision , vol. 131, no. 2, pp. 531–551, 2023
2023
Later among the works it cites.
Y. Chen, J. Liu, X. Zhang, X. Qi, and J. Jia, “Largekernel3d: Scaling up kernels in 3d sparse cnns,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 13 488–13 498
2023
Later among the works it cites.
T. Lu, X. Ding, H. Liu, G. Wu, and L. Wang, “Link: Linear kernel for lidar-based 3d perception,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 1105–1115
2023
Later among the works it cites.
X. Lai, Y. Chen, F. Lu, J. Liu, and J. Jia, “Spherical transformer for lidar-based 3d recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 17 545–17 555
2023
Later among the works it cites.
D. Zhang, D. Liang, Z. Zou, J. Li, X. Ye, Z. Liu, X. Tan, and X. Bai, “A simple vision transformer for weakly semi-supervised 3d object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 8373–8383
2023
Later among the works it cites.
X. Feng, H. Du, H. Fan, Y. Duan, and Y. Liu, “Seformer: Structure embedding transformer for 3d object detection,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 1, 2023, pp. 632–640
2023
Later among the works it cites.
H. Wu, C. Wen, S. Shi, X. Li, and C. Wang, “Virtual sparse convolution for multimodal 3d object detection,” Mar 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
Z. Yu, W. Wan, M. Ren, X. Zheng, and Z. Fang, “Sparsefusion3d: Sparse sensor fusion for 3d object detection by radar and camera in environmental perception,” IEEE Transactions on Intelligent Vehicles , pp. 1–14, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
Z. Liu, T. Huang, B. Li, X. Chen, X. Wang, and X. Bai, “Epnet++: Cascade bi-directional fusion for multi-modal 3d object detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 7, pp. 8324–8341, 2023
2023
Later among the works it cites.
Z. Song, H. Wei, L. Bai, L. Yang, and C. Jia, “Graphalign: Enhancing accurate feature alignment by graph matching for multi-modal 3d object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 3358–3369
2023
Later among the works it cites.
Z. Song, C. Jia, L. Yang, H. Wei, and L. Liu, “Graphalign++: An accurate feature alignment by graph matching for multi-modal 3d object detection,” IEEE Transactions on Circuits and Systems for Video Technology , 2023
2023
Later among the works it cites.
Q. Cai, Y. Pan, T. Yao, C.-W. Ngo, and T. Mei, “Objectfusion: Multi-modal 3d object detection with object-centric fusion,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2023, pp. 18 067–18 076
2023
Later among the works it cites.
Y. Qin, C. Wang, Z. Kang, N. Ma, Z. Li, and R. Zhang, “Supfusion: Supervised lidar-camera fusion for 3d object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2023, pp. 22 014–22 024
2023
Later among the works it cites.
X. Li, T. Ma, Y. Hou, B. Shi, Y. Yang, Y. Liu, X. Wu, Q. Chen, Y. Li, Y. Qiao, and L. He, “Logonet: Towards accurate 3d object detection with local-to-global cross-modal fusion,” Mar 2023
2023
Later among the works it cites.
Z. Song, G. Zhang, J. Xie, L. Liu, C. Jia, S. Xu, and Z. Wang, “Voxelnextfusion: A simple, unified, and effective voxel fusion framework for multimodal 3-d object detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 61, pp. 1–12, 2023
2023
Later among the works it cites.
Y. Wang, Q. Mao, H. Zhu, J. Deng, Y. Zhang, J. Ji, H. Li, and Y. Zhang, “Multi-modal 3d object detection in autonomous driving: a survey,” International Journal of Computer Vision , pp. 1–31, 2023
2023
Later among the works it cites.
S. Xie, L. Kong, W. Zhang, J. Ren, L. Pan, K. Chen, and Z. Liu, “Robobev: Towards robust bird’s eye view perception under corruptions,” Apr 2023
2023
Later among the works it cites.
Y. Dong, C. Kang, J. Zhang, Z. Zhu, Y. Wang, X. Yang, H. Su, X. Wei, and J. Zhu, “Benchmarking robustness of 3d object detection to common corruptions in autonomous driving,” Mar 2023
2023
Later among the works it cites.
J. Mao, S. Shi, X. Wang, and H. Li, “3d object detection for autonomous driving: A comprehensive survey,” International Journal of Computer Vision , pp. 1–55, 2023
2023
Later among the works it cites.
S. Y. Alaba and J. E. Ball, “Deep learning-based image 3-d object detection for autonomous driving,” IEEE Sensors Journal , vol. 23, no. 4, pp. 3378–3394, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
X. Wang, K. Li, and A. Chehri, “Multi-sensor fusion technology for 3d object detection in autonomous driving: A review,” IEEE Transactions on Intelligent Transportation Systems , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
S. Li, Z. Wang, F. Juefei-Xu, Q. Guo, X. Li, and L. Ma, “Common corruption robustness of point cloud detectors: Benchmark and enhancement,” IEEE Transactions on Multimedia , 2023
2023
Later among the works it cites.
L. Kong, Y. Liu, X. Li, R. Chen, W. Zhang, J. Ren, L. Pan, K. Chen, and Z. Liu, “Robo3d: Towards robust and reliable 3d perception against corruptions,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 19 994–20 006
2023
Later among the works it cites.
2023
Later among the works it cites.
Z. Wu, Y. Gan, L. Wang, G. Chen, and J. Pu, “Monopgc: Monocular 3d object detection with pixel geometry contexts,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 4842–4849
2023
Later among the works it cites.
M. Zhu, L. Ge, P. Wang, and H. Peng, “Monoedge: Monocular 3d object detection using local perspectives,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2023, pp. 643–652
2023
Later among the works it cites.
F. Yang, X. Xu, H. Chen, Y. Guo, Y. He, K. Ni, and G. Ding, “Gpro3d: Deriving 3d bbox from ground plane in monocular 3d object detection,” Neurocomputing , vol. 562, p. 126894, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Z. Min, B. Zhuang, S. Schulter, B. Liu, E. Dunn, and M. Chandraker, “Neurocs: Neural nocs supervision for monocular 3d object localization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 21 404–21 414
2023
Later among the works it cites.
X. Liu, C. Zheng, K. B. Cheng, N. Xue, G.-J. Qi, and T. Wu, “Monocular 3d object detection with bounding box denoising in 3d by perceiver,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 6436–6446
2023
Later among the works it cites.
J. Xu, L. Peng, H. Cheng, H. Li, W. Qian, K. Li, W. Wang, and D. Cai, “Mononerd: Nerf-like representations for monocular 3d object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 6814–6824
2023
Later among the works it cites.
R. Tao, W. Han, Z. Qiu, C.-z. Xu, and J. Shen, “Weakly supervised monocular 3d object detection using multi-view projection and direction consistency,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 17 482–17 492
2023
Later among the works it cites.
X. Wu, D. Ma, X. Qu, X. Jiang, and D. Zeng, “Depth dynamic center difference convolutions for monocular 3d object detection,” Neurocomputing , vol. 520, pp. 73–81, 2023
2023
Later among the works it cites.
C. Huang, T. He, H. Ren, W. Wang, B. Lin, and D. Cai, “Obmo: One bounding box multiple objects for monocular 3d object detection,” IEEE Transactions on Image Processing , vol. 32, pp. 6570–6581, 2023
2023
Later among the works it cites.
L. Yang, X. Zhang, J. Li, L. Wang, M. Zhu, and L. Zhu, “Lite-fpn for keypoint-based monocular 3d object detection,” Knowledge-Based Systems , vol. 271, p. 110517, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
G. Brazil, A. Kumar, J. Straub, N. Ravi, J. Johnson, and G. Gkioxari, “Omni3d: A large benchmark and model for 3d object detection in the wild,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2023, pp. 13 154–13 164
2023
Later among the works it cites.
J. U. Kim, H.-I. Kim, and Y. M. Ro, “Stereoscopic vision recalling memory for monocular 3d object detection,” IEEE Transactions on Image Processing , 2023
2023
Later among the works it cites.
Z. Wu, Y. Wu, J. Pu, X. Li, and X. Wang, “Attention-based depth distillation with 3d-aware positional encoding for monocular 3d object detection,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 3, 2023, pp. 2892–2900
2023
Later among the works it cites.
H. Sheng, S. Cai, N. Zhao, B. Deng, M.-J. Zhao, and G. H. Lee, “Pdr: Progressive depth regularization for monocular 3d object detection,” IEEE Transactions on Circuits and Systems for Video Technology , 2023
2023
Later among the works it cites.
C. Tao, J. Cao, C. Wang, Z. Zhang, and Z. Gao, “Pseudo-mono for monocular 3d object detection in autonomous driving,” IEEE Transactions on Circuits and Systems for Video Technology , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
O.-H. Kwon and E. Zell, “Image-coupled volume propagation for stereo matching,” in 2023 IEEE International Conference on Image Processing (ICIP) . IEEE, 2023, pp. 2510–2514
2023
Later among the works it cites.
Z. Shen, X. Song, Y. Dai, D. Zhou, Z. Rao, and L. Zhang, “Digging into uncertainty-based pseudo-label for robust stereo matching,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
2023
Later among the works it cites.
G. Xu, X. Wang, X. Ding, and X. Yang, “Iterative geometry encoding volume for stereo matching,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 21 919–21 928
2023
Later among the works it cites.
L. Yang, K. Yu, T. Tang, J. Li, K. Yuan, L. Wang, X. Zhang, and P. Chen, “Bevheight: A robust framework for vision-based roadside 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 21 611–21 620
2023
Later among the works it cites.
X. Chi, J. Liu, M. Lu, R. Zhang, Z. Wang, Y. Guo, and S. Zhang, “Bev-san: Accurate bev 3d object detection via slice attention networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 17 461–17 470
2023
Later among the works it cites.
Y. Li, H. Bao, Z. Ge, J. Yang, J. Sun, and Z. Li, “Bevstereo: Enhancing depth estimation in multi-view 3d object detection with temporal stereo,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 2, 2023, pp. 1486–1494
2023
Later among the works it cites.
2023
Later among the works it cites.
S. Wang, X. Zhao, H.-M. Xu, Z. Chen, D. Yu, J. Chang, Z. Yang, and F. Zhao, “Towards domain generalization for multi-view 3d object detection in bird-eye-view,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 13 333–13 342
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Jiang, L. Zhang, Z. Miao, X. Zhu, J. Gao, W. Hu, and Y.-G. Jiang, “Polarformer: Multi-camera 3d object detection with polar transformer,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 1, 2023, pp. 1042–1050
2023
Later among the works it cites.
Y. Wang, Y. Chen, and Z. Zhang, “Frustumformer: Adaptive instance-aware resampling for multi-view 3d detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 5096–5105
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
K. Xiong, S. Gong, X. Ye, X. Tan, J. Wan, E. Ding, J. Wang, and X. Bai, “Cape: Camera view position embedding for multi-view 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 21 570–21 579
2023
Later among the works it cites.
D. Chen, J. Li, V. Guizilini, R. A. Ambrus, and A. Gaidon, “Viewpoint equivariance for multi-view 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 9213–9222
2023
Later among the works it cites.
C. Shu, J. Deng, F. Yu, and Y. Liu, “3dppe: 3d point positional encoding for transformer-based multi-camera 3d object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 3580–3589
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
C. Yang, Y. Chen, H. Tian, C. Tao, X. Zhu, Z. Zhang, G. Huang, H. Li, Y. Qiao, L. Lu et al. , “Bevformer v2: Adapting modern image backbones to bird’s-eye-view recognition via perspective supervision,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 17 830–17 839
2023
Later among the works it cites.
Y. Zhou, H. Zhu, Q. Liu, S. Chang, and M. Guo, “Monoatt: Online monocular 3d object detection with adaptive token transformer,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 17 493–17 503
2023
Later among the works it cites.
2023
Later among the works it cites.
Z. Wang, D. Li, C. Luo, C. Xie, and X. Yang, “Distillbev: Boosting multi-camera 3d object detection with cross-modal knowledge distillation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 8637–8646
2023
Later among the works it cites.
I. Koo, I. Lee, S.-H. Kim, H.-S. Kim, W.-j. Jeon, and C. Kim, “Pg-rcnn: Semantic surface point generation for 3d object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 18 142–18 151
2023
Later among the works it cites.
H. Zhang, G. Luo, X. Wang, Y. Li, W. Ding, and F.-Y. Wang, “Sasan: Shape-adaptive set abstraction network for point-voxel 3d object detection,” IEEE Transactions on Neural Networks and Learning Systems , 2023
2023
Later among the works it cites.
H. Yang, W. Wang, M. Chen, B. Lin, T. He, H. Chen, X. He, and W. Ouyang, “Pvt-ssd: Single-stage 3d object detector with point-voxel transformer,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 13 476–13 487
2023
Later among the works it cites.
B. Fan, K. Zhang, and J. Tian, “Hcpvf: Hierarchical cascaded point-voxel fusion for 3d object detection,” IEEE Transactions on Circuits and Systems for Video Technology , 2023
2023
Later among the works it cites.
J. Cao, C. Tao, Z. Zhang, Z. Gao, X. Luo, S. Zheng, and Y. Zhu, “Accelerating point-voxel representation of 3d object detection for automatic driving,” IEEE Transactions on Artificial Intelligence , 2023
2023
Later among the works it cites.
C. Feng, C. Xiang, X. Xie, Y. Zhang, M. Yang, and X. Li, “Hpv-rcnn: Hybrid point–voxel two-stage network for lidar based 3-d object detection,” IEEE Transactions on Computational Social Systems , 2023
2023
Later among the works it cites.
S. Xu, F. Li, Z. Song, J. Fang, S. Wang, and Z.-X. Yang, “Multi-sem fusion: Multimodal semantic fusion for 3d object detection,” 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
Z. Liu, H. Tang, A. Amini, X. Yang, H. Mao, D. L. Rus, and S. Han, “Bevfusion: Multi-task multi-sensor fusion with unified bird’s-eye view representation,” pp. 2774–2781, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
“Focusing on hard instance for 3d object detection,” Aug 2023
2023
Later among the works it cites.
H. Wang, H. Tang, S. Shi, A. Li, Z. Li, B. Schiele, and L. Wang, “Unitr: A unified and efficient multi-modal transformer for bird’s-eye-view representation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 6792–6802
2023
Later among the works it cites.
H. Meng, C. Li, G. Chen, L. Chen et al. , “Efficient 3d object detection based on pseudo-lidar representation,” IEEE Transactions on Intelligent Vehicles , 2023
2023
Later among the works it cites.
C. Xu, B. Wu, J. Hou, S. Tsai, R. Li, J. Wang, W. Zhan, Z. He, P. Vajda, K. Keutzer et al. , “Nerf-det: Learning geometry-aware volumetric representation for multi-view 3d object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 23 320–23 330
2023
Later among the works it cites.
H. Li, C. Sima, J. Dai, W. Wang, L. Lu, H. Wang, J. Zeng, Z. Li, J. Yang, H. Deng et al. , “Delving into the devils of bird’s-eye-view perception: A review, evaluation and recipe,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
2023
Later among the works it cites.
H. Meng, C. Li, G. Chen, Z. Gu, and A. Knoll, “Er3d: An efficient real-time 3d object detection framework for autonomous driving,” in 29th IEEE International Conference on Parallel and Distributed Systems , 2023
2023
Later among the works it cites.
H. Meng, C. Li, C. Zhong, J. Gu, G. Chen, and A. Knoll, “Fastfusion: Deep stereo-lidar fusion for real-time high-precision dense depth sensing,” Journal of Field Robotics , vol. 40, no. 7, pp. 1804–1816, 2023
2023
Later among the works it cites.
Z. Zhu, Y. Zhang, H. Chen, Y. Dong, S. Zhao, W. Ding, J. Zhong, and S. Zheng, “Understanding the robustness of 3d object detection with bird’s-eye-view representations in autonomous driving,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 21 600–21 610
2023
Later among the works it cites.
Y. Zhang, J. Hou, and Y. Yuan, “A comprehensive study of the robustness for lidar-based 3d object detectors against adversarial attacks,” International Journal of Computer Vision , pp. 1–33, 2023
2023
Later among the works it cites.
Q. Hu, D. Liu, and W. Hu, “Density-insensitive unsupervised domain adaption on 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 17 556–17 566
2023
Later among the works it cites.
J. Yuan, B. Zhang, X. Yan, T. Chen, B. Shi, Y. Li, and Y. Qiao, “Bi3d: Bi-domain active learning for cross-domain 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 15 599–15 608
2023
Later among the works it cites.
B. Ding, J. Xie, and J. Nie, “C2bn: Cross-modality and cross-scale balance network for multi-modal 3d object detection,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2023, pp. 1–5
2023
Later among the works it cites.
C. Zhang, H. Wang, L. Chen, Y. Li, and Y. Cai, “Mixedfusion: An efficient multimodal data fusion framework for 3-d object detection and tracking,” IEEE Transactions on Neural Networks and Learning Systems , pp. 1–15, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
L. Zheng, S. Li, B. Tan, L. Yang, S. Chen, L. Huang, J. Bai, X. Zhu, and Z. Ma, “Rcfusion: Fusing 4d radar and camera with bird’s-eye view features for 3d object detection,” IEEE Transactions on Instrumentation and Measurement , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
T. Zhou, J. Chen, Y. Shi, K. Jiang, M. Yang, and D. Yang, “Bridging the view disparity between radar and camera features for multi-modal fusion 3d object detection,” IEEE Transactions on Intelligent Vehicles , vol. 8, no. 2, pp. 1523–1535, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo et al. , “Segment anything,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 4015–4026
2023
Later among the works it cites.
Y. Hu, J. Yang, L. Chen, K. Li, C. Sima, X. Zhu, S. Chai, S. Du, T. Lin, W. Wang et al. , “Planning-oriented autonomous driving,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 17 853–17 862
2023
Later among the works it cites.
C. Xia, W. Zhao, H. Han, Z. Tao, B. Ge, X. Gao, K.-C. Li, and Y. Zhang, “Monosaid: Monocular 3d object detection based on scene-level adaptive instance depth estimation,” Journal of Intelligent & Robotic Systems , vol. 110, no. 1, p. 2, 2024
2024
Closest in time.
C. Park, H. Kim, J. Jang, and J. Paik, “Odd-m3d: Object-wise dense depth estimation for monocular 3d object detection,” IEEE Transactions on Consumer Electronics , 2024
2024
Closest in time.
2024
Closest in time.
W. Zhang, D. Liu, C. Ma, and W. Cai, “Alleviating foreground sparsity for semi-supervised monocular 3d object detection,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2024, pp. 7542–7552
2024
Closest in time.
Z. Wu, Y. Gan, Y. Wu, R. Wang, X. Wang, and J. Pu, “Fd3d: Exploiting foreground depth map for feature-supervised monocular 3d object detection,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 6, 2024, pp. 6189–6197
2024
Closest in time.
C.-H. Wang, H.-W. Chen, Y. Chen, P.-Y. Hsiao, and L.-C. Fu, “Vopifnet: Voxel-pixel fusion network for multi-class 3d object detection,” IEEE Transactions on Intelligent Transportation Systems , pp. 1–11, 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
T. Yuan, J. Hu, S. Ou, W. Yang, and Y. Hei, “Hourglass cascaded recurrent stereo matching network,” Image and Vision Computing , p. 105074, 2024
2024
Closest in time.
2024
Closest in time.
P. Dong, Z. Kong, X. Meng, P. Yu, Y. Gong, G. Yuan, H. Tang, and Y. Wang, “Hotbev: Hardware-oriented transformer-based multi-view 3d detector for bev perception,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
2024
Closest in time.
L. Yan, P. Yan, S. Xiong, X. Xiang, and Y. Tan, “Monocd: Monocular 3d object detection with complementary depths,” in CVPR , 2024
2024
Closest in time.
G. Xie, Z. Chen, M. Gao, M. Hu, and X. Qin, “Ppf-det: Point-pixel fusion for multi-modal 3d object detection,” IEEE Transactions on Intelligent Transportation Systems , 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Y. Li, L. Fan, Y. Liu, Z. Huang, Y. Chen, N. Wang, and Z. Zhang, “Fully sparse fusion for 3d object detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence , pp. 1–15, 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
M. Liu, Y. Chen, J. Xie, Y. Zhu, Y. Zhang, L. Yao, Z. Bing, G. Zhuang, K. Huang, and J. T. Zhou, “Menet: Multi-modal mapping enhancement network for 3d object detection in autonomous driving,” IEEE Transactions on Intelligent Transportation Systems , 2024
2024
Closest in time.
Z. Wu, Y. Wu, X. Wang, Y. Gan, and J. Pu, “A robust diffusion modeling framework for radar camera 3d object detection,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2024, pp. 3282–3292
2024
Closest in time.
J. Hou, Z. Liu, Z. Zou, X. Ye, X. Bai et al. , “Query-based temporal fusion with explicit motion for 3d object detection,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
S. Jiang, S. Xu, L. Liu, Z. Song, Y. Bo, Z.-X. Yang et al. , “Sparseinteraction: Sparse semantic guidance for radar and camera 3d object detection,” in ACM Multimedia 2024
2024
Closest in time.
Y. Liu, L. Kong, J. Cen, R. Chen, W. Zhang, L. Pan, K. Chen, and Z. Liu, “Segment any point cloud sequences by distilling vision foundation models,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
2024
Closest in time.
F. Chabot, M. Chaouch, J. Rabarisoa, C. Teuliere, 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, pp. 2040–2049
2049
Closest in time.
F. Manhardt, W. Kehl, and A. Gaidon, “Roi-10d: Monocular lifting of 2d detection to 6d pose and metric shape,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 2069–2078
2078
Closest in time.