Bayesian Optimization of 3D Feature Parameters for 6D Pose Estimation

Frederik Hagelskjær*, Norbert Krüger, Anders Glent Buch

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

149 Downloads (Pure)


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 languageEnglish
Title of host publicationProceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
EditorsAndreas Kerren, Christophe Hurter, Jose Braz
Volume5: VISAPP
PublisherSCITEPRESS Digital Library
Publication date2019
ISBN (Electronic)978-989-758-354-4
Publication statusPublished - 2019
Event14th International Conference on Computer Vision Theory and Applications - Prague, Czech Republic
Duration: 25. Feb 201927. Feb 2019


Conference14th International Conference on Computer Vision Theory and Applications
CountryCzech Republic


  • 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

Fingerprint Dive into the research topics of 'Bayesian Optimization of 3D Feature Parameters for 6D Pose Estimation'. Together they form a unique fingerprint.

Cite this