MONO6D: Monocular Vehicle 6D Pose Estimation with 3D Priors
Author:
Affiliation:
1. Vrije Universiteit,Department of Electronics and Informatics,Brussel
Funder
Fonds Wetenschappelijk Onderzoek
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9897158/9897159/09897311.pdf?arnumber=9897311
Reference18 articles.
1. Pointnet: Deep learning on point sets for 3d classification and segmentation;qi;CVPR,2017
2. Deep residual learning for image recognition;he;CVPR,2016
3. 6D-VNet: End-To-End 6DoF Vehicle Pose Estimation From Monocular RGB Images
4. ApolloCar3D: A Large 3D Car Instance Understanding Benchmark for Autonomous Driving
5. Pose from shape: Deep pose estimation for arbitrary 3D objects;xiao;BMVC,2019
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1. Research on Vehicle Pose Detection Method Based on a Roadside Unit;Sensors;2024-07-21
2. MonoKalman: Monocular Vehicle Pose Estimation with Kalman Filter-based temporal consistency;2024 25th IEEE International Conference on Mobile Data Management (MDM);2024-06-24
3. DeepKalPose: An enhanced deep‐learning Kalman filter for temporally consistent monocular vehicle pose estimation;Electronics Letters;2024-04
4. W6DNet: Weakly Supervised Domain Adaptation for Monocular Vehicle 6-D Pose Estimation With 3-D Priors and Synthetic Data;IEEE Transactions on Instrumentation and Measurement;2024
5. RESSCAL3D: Resolution Scalable 3D Semantic Segmentation of Point Clouds;2023 IEEE International Conference on Image Processing (ICIP);2023-10-08
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