[35] Yingfei Liu, Tiancai Wang, Xiangyu Zhang, and Jian Sun. Petr: Position embedding transformation for multi-view 3d object detection. In Proceedings of the European Conference on Computer Vision, 2022.
[36] Zhijian Liu, Haotian Tang, Alexander Amini, Xinyu Yang, Huizi Mao, Daniela L Rus, and Song Han. Bev-fusion: Multi-task multi-sensor fusion with unified bird's-eye view representation. In IEEE International Conference on Robotics and Automation, 2023.
[37] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations, 2019.
[38] Shujie Luo, Hang Dai, Ling Shao, and Yong Ding. M3dssd: Monocular 3d single stage object detector. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021.
[39] Xinzhu Ma, Yinmin Zhang, Dan Xu, Dongzhan Zhou, Shuai Yi, Haojie Li, and Wanli Ouyang. Delving into localization errors for monocular 3d object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021.
[40] Dennis Park, Rares Ambrus, Vitor Guizilini, Jie Li, and Adrien Gaidon. Is pseudo-lidar needed for monocular 3d object detection? In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021.
[41] Jinhyung Park, Chenfeng Xu, Shijia Yang, Kurt Keutzer, Kris Kitani, Masayoshi Tomizuka, and Wei Zhan. Time will tell: New outlooks and a baseline for temporal multi-view 3d object detection. In International Conference on Learning Representations, 2023.
[42] Youngmin Park, Vincent Lepetit, and Woontack Woo. Multiple 3d object tracking for augmented reality. In IEEE/ACM International Symposium on Mixed and Augmented Reality, 2008.
[43] Jonah Philion and Sanja Fidler. Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d. In Proceedings of the European Conference on Computer Vision, 2020.
[44] Rui Qian, Xin Lai, and Xirong Li. 3d object detection for autonomous driving: a survey. Pattern Recognition, 130:108796, 2022.
[45] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, 2021.
[46] Kavya Ravichandran, Ajay Jain, and Alexander Rakhlin. Using effective dimension to analyze feature transformations in deep neural networks. In ICML 2019 Workshop on Identifying and Understanding Deep Learning Phenomena, 2019.
[47] Cody Reading, Ali Harakeh, Julia Chae, and Steven 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.
[48] Shaoshuai Shi, Chaoxu Guo, Li Jiang, Zhe Wang, Jianping Shi, Xiaogang Wang, and Hongsheng 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.
[49] Shaoshuai Shi, Xiaogang Wang, and Hongsheng 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.
[50] Andrea Simonelli, Samuel Rota Bulo, Lorenzo Porzi, Peter Kontschieder, and Elisa Ricci. Are we missing confidence in pseudo-lidar methods for monocular 3d object detection? In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021.
[51] Chunwei Wang, Chao Ma, Ming Zhu, and Xiaokang Yang. Pointaugmenting: Cross-modal augmentation for 3d object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021.
[52] Tai Wang, ZHU Xinge, Jiangmiao Pang, and Dahua Lin. Probabilistic and geometric depth: Detecting objects in perspective. In Proceedings of the Conference on Robot Learning, 2022.
[53] Tai Wang, Xinge Zhu, Jiangmiao Pang, and Dahua Lin. Fcos3d: Fully convolutional one-stage monocular 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021.