Lite-FPN for keypoint-based monocular 3D object detection

Author:

Yang LeiORCID,Zhang XinyuORCID,Li Jun,Wang LiORCID,Zhu MinghanORCID,Zhu Lei

Funder

China Postdoctoral Science Foundation

National High-tech Research and Development Program

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Management Information Systems,Software

Reference64 articles.

1. Y. Wang, W.-L. Chao, D. Garg, B. Hariharan, M. Campbell, K.Q. Weinberger, Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 8445–8453.

2. Pseudo-lidar++: Accurate depth for 3d object detection in autonomous driving;You,2019

3. Integration of ontology reasoning-based monocular cues in deep learning modeling for single image depth estimation in urban driving scenarios;Benkirane;Knowl.-Based Syst.,2022

4. Single image depth estimation based on sculpture strategy;Chen;Knowl.-Based Syst.,2022

5. SAT-GCN: Self-attention graph convolutional network-based 3D object detection for autonomous driving;Wang;Knowl.-Based Syst.,2023

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