Weakly Supervised Multi-Modal 3D Human Body Pose Estimation for Autonomous Driving
Author:
Affiliation:
1. University of Stuttgart,Stuttgart,70174
2. Friedrich-Alexander-Universität Erlangen-Nuernberg,Erlangen,Germany,91058
3. Ulm University,Ulm,Germany,89081
4. Munich University of Applied Sciences,Intelligent Vehicles Lab,Munich,Germany,80335
Funder
Delta
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10186382/10186383/10186575.pdf?arnumber=10186575
Reference40 articles.
1. Hum3dil: Semi-supervised multi-modal 3d human pose estimation for autonomous driving;zanfir,2022
2. Adam: A method for stochastic optimization;kingma,2014
3. Scalability in Perception for Autonomous Driving: Waymo Open Dataset
4. Self-Supervised Learning of 3D Human Pose Using Multi-View Geometry
5. UrbanPose: A New Benchmark for VRU Pose Estimation in Urban Traffic Scenes
Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. LPFormer: LiDAR Pose Estimation Transformer with Multi-Task Network;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13
2. MotionAGFormer: Enhancing 3D Human Pose Estimation with a Transformer-GCNFormer Network;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03
3. Efficient, Self-Supervised Human Pose Estimation with Inductive Prior Tuning;2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW);2023-10-02
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