Homography Loss for Monocular 3D Object Detection
Author:
Affiliation:
1. Alibaba Cloud Computing Ltd.
2. Zhejiang University
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9878378/9878366/09878651.pdf?arnumber=9878651
Reference45 articles.
1. Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving
2. Probabilistic and geometric depth: Detecting objects in per-spective;wang;5th Annual Conference on Robot Learning,2021
3. Ortho-graphic feature transform for monocular 3d object detection;roddick;ArXiv Preprint,2018
4. Categorical Depth Distribution Network for Monocular 3D Object Detection
5. Monogrnet: A geo-metric reasoning network for monocular 3d object localization;qin;Proceedings of the AAAI Conference on Artificial Intelligence,2019
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1. Radar-camera fusion for 3D object detection with aggregation transformer;Applied Intelligence;2024-08-22
2. CrossHomo: Cross-Modality and Cross-Resolution Homography Estimation;IEEE Transactions on Pattern Analysis and Machine Intelligence;2024-08
3. ATV3D: 3D Object Detection from Attention-based Three-view Representation;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30
4. DAGM-Mono: Deformable Attention-Guided Modeling for Monocular 3D Reconstruction;Optical Memory and Neural Networks;2024-06
5. A survey on 3D object detection in real time for autonomous driving;Frontiers in Robotics and AI;2024-03-06
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