Multi-view object pose estimation from correspondence distributions and epipolar geometry
Author:
Affiliation:
1. SDU Robotics, Maersk Mc-Kinney Moller Institute, University of Southern Denmark.
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10160211/10160212/10161514.pdf?arnumber=10161514
Reference30 articles.
1. A method for registration of 3-D shapes
2. Random sample consensus
3. BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth
4. T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-Less Objects
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1. Fixture calibration with guaranteed bounds from a few correspondence-free surface points;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13
2. A Survey of 6DoF Object Pose Estimation Methods for Different Application Scenarios;Sensors;2024-02-07
3. SpyroPose: SE(3) Pyramids for Object Pose Distribution Estimation;2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW);2023-10-02
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