[46] Yan Wang, Wei-Lun Chao, Divyansh Garg, Bharath Hariharan, Mark Campbell, and Kilian 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 (CVPR), June 2019.
[47] Xiaopei Wu, Liang Peng, Honghui Yang, Liang Xie, Chenxi Huang, Chengqi Deng, Haifeng Liu, and Deng Cai. Sparse fuse dense: Towards high quality 3d detection with depth completion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5418-5427, June 2022.
[48] Tianfu Wu Xianpeng Liu, Nan Xue. Learning auxiliary monocular contexts helps monocular 3d object detection. In 36th AAAI Conference on Artificial Intelligence (AAAI), Feburary 2022.
[49] Lei Yang, Kaicheng Yu, Tao Tang, Jun Li, Kun Yuan, Li Wang, Xinyu Zhang, and Peng Chen. Bevheight: A robust framework for vision-based roadside 3d object detection. In IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), Mar. 2023.
[50] Lei Yang, Xinyu Zhang, Li Wang, Minghan Zhu, Chuan-Fang Zhang, and Jun Li. Mix-teaching: A simple, unified and effective semi-supervised learning framework for monocular 3d object detection. ArXiv, abs/2207.04448, 2022.
[51] Zetong Yang, Yanan Sun, Shu Liu, Xiaoyong Shen, and Jiaya Jia. Std: Sparse-to-dense 3d object detector for point cloud. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2019.
[52] Xiaoqing Ye, Liang Du, Yifeng Shi, Yingying Li, Xiao Tan, Jianfeng Feng, Errui Ding, and Shilei Wen. Monocular 3d object detection via feature domain adaptation. In European Conference on Computer Vision, pages 17-34. Springer, 2020.
[53] Xiaoqing Ye, Mao Shu, Hanyu Li, Yifeng Shi, Yingying Li, Guangjie Wang, Xiao Tan, and Errui Ding. Rope3d: The roadside perception dataset for autonomous driving and monocular 3d object detection task. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 21341-21350, June 2022.
[54] Fisher Yu, Dequan Wang, Evan Shelhamer, and Trevor Darrell. Deep layer aggregation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018.
[55] Haibao Yu, Yizhen Luo, Mao Shu, Yiyi Huo, Zebang Yang, Yifeng Shi, Zhenglong Guo, Hanyu Li, Xing Hu, Jirui Yuan, and Zaijing Nie. Dair-v2x: A large-scale dataset for vehicle-infrastructure cooperative 3d object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 21361-21370, June 2022.
[56] Yunpeng Zhang, Jiwen Lu, and Jie Zhou. Objects are different: Flexible monocular 3d object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3289-3298, June 2021.
[57] Xingyi Zhou, Dequan Wang, and Philipp Krahenbuhl. Objects as points. In arXiv preprint arXiv:1904.07850, 2019.
[58] Yunsong Zhou, Yuan He, Hongzi Zhu, Cheng Wang, Hongyang Li, and Qinhong Jiang. Monocular 3d object detection: An extrinsic parameter free approach. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7556-7566, 2021.
[59] Yunsong Zhou, Quan Liu, Hongzi Zhu, Yunzhe Li, Shan Chang, and Minyi Guo. Mogde: Boosting mobile monocular 3d object detection with ground depth estimation, 2023.
[60] Yunsong Zhou, Hongzi Zhu, Quan Liu, Shan Chang, and Minyi Guo. Monoatt: Online monocular 3d object detection with adaptive token transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 17493-17503, June 2023.
[61] Zhikang Zou, Xiaoqing Ye, Liang Du, Xianhui Cheng, Xiao Tan, Li Zhang, Jianfeng Feng, Xiangyang Xue, and Errui Ding. The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 2713-2722, October 2021.