6D Object Pose Estimation with Pairwise Compatible Geometric Features
Name
ICRA2021PoseEstimation.pdf
Size
4.47 MB
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Unknown
Checksum (MD5)
813f9af6b5f671c8db2bfe4e45f301d1
Author(s)
Lin, Muyuan
Murali, Varun
Karaman, Sertac
Date Issued
May 30, 2021
Publisher
IEEE
Citation
Lin, Muyuan, Murali, Varun and Karaman, Sertac. 2021. "6D Object Pose Estimation with Pairwise Compatible Geometric Features."
Version
Author's final manuscript
Abstract
This work addresses the problem of 6-DoF pose estimation under heavy occlusion. While previous work demonstrates reasonable results in unoccluded situations, robust and efficient pose estimation is still challenging in heavily occluded and low-texture scenarios which are ubiquitous in many applications. To this end, we propose a novel end-to-end deep neural network model recovering object poses from depth measurements. The proposed model enforces pairwise consistency of 3D geometric features by applying spectral convolutions on a pairwise compatibility graph. We achieve comparable accuracy as the state-of-the-art graph matching solver while being much faster. Our approach outperforms state-of-the-art 6-DoF pose estimation methods on LineMOD and Occlusion LineMOD and runs in reasonable time (~5.9 Hz). We additionally verify this method on a synthetic dataset with large affine changes.
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/icra48506.2021.9561404
