6D Object Pose Estimation with Pairwise Compatible Geometric Features
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 applicati...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE,
2021-11-12T15:28:30Z.
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| Subjects: | |
| Online Access: | Get fulltext |
| Summary: | 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. |
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