Abstract
6D pose estimation using local features has shown state-of-the-art performance for object recognition and pose estimation from 3D data in a number of benchmarks. However, this method requires extensive knowledge and elaborate parameter tuning to obtain optimal performances. In this paper, we propose an optimization method able to determine feature parameters automatically, providing improved point matches to a robust pose estimation algorithm. Using labeled data, our method measures the performance of the current parameter setting using a scoring function based on both true and false positive detections. Combined with a Bayesian optimization strategy, we achieve automatic tuning using few labeled examples. Experiments were performed on two recent RGB-D benchmark datasets. The results show significant improvements by tuning an existing algorithm, with state-of-art performance.
Original language | English |
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Title of host publication | Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
Editors | Andreas Kerren, Christophe Hurter, Jose Braz |
Volume | 5: VISAPP |
Publisher | SCITEPRESS Digital Library |
Publication date | 2019 |
Pages | 135-142 |
ISBN (Electronic) | 978-989-758-354-4 |
DOIs | |
Publication status | Published - 2019 |
Event | 14th International Conference on Computer Vision Theory and Applications - Prague, Czech Republic Duration: 25. Feb 2019 → 27. Feb 2019 |
Conference
Conference | 14th International Conference on Computer Vision Theory and Applications |
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Country | Czech Republic |
City | Prague |
Period | 25/02/2019 → 27/02/2019 |
Keywords
- Machine Learning Technologies for Vision
- Shape Representation and Matching
- Features Extraction
- Object Detection and Localization
- Bayesian Optimization
- Object Detection
- Pose Estimation
- Machine Learning
- Optimization
- Feature Matching