Investigating Keypoint Repeatability for 3D Correspondence Estimation in Cluttered Scenes

Chiem, Q, Wilkinson, R, Lech, M and Cheng, E 2017, 'Investigating Keypoint Repeatability for 3D Correspondence Estimation in Cluttered Scenes', in Proceedings of the 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2017), Sydney, Australia, 29 November - 1 December 2017, pp. 134-139.


Document type: Conference Paper
Collection: Conference Papers

Title Investigating Keypoint Repeatability for 3D Correspondence Estimation in Cluttered Scenes
Author(s) Chiem, Q
Wilkinson, R
Lech, M
Cheng, E
Year 2017
Conference name DICTA 2017
Conference location Sydney, Australia
Conference dates 29 November - 1 December 2017
Proceedings title Proceedings of the 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2017)
Publisher IEEE
Place of publication United States
Start page 134
End page 139
Total pages 6
Abstract In 3D object recognition, local feature-based recognition is known to be robust against occlusion and clutter. Local feature estimation requires feature correspondences, including feature extraction and matching. Feature extraction is normally a two-stage process that estimates keypoints and keypoint descriptors, and existing studies show repeatability to be a good indicator of keypoint feature detector robustness. However, the impact of keypoint repeatability on feature correspondence estimation and overall feature matching accuracy has not yet been studied. In this paper, local features are extracted at both regular and repeatable 3D keypoints using leading keypoint detectors combined with the SHOT descriptor to estimate a set of correspondences. When using a keypoint detector of high repeatability, experimental results show improved feature matching accuracy and reduced computational requirements for the feature description and matching, and overall correspondence estimation process.
Subjects Signal Processing
Keyword(s) feature extraction
image matching
object detection
object recognition
DOI - identifier 10.1109/DICTA.2017.8227449
Copyright notice © 2017 IEEE
ISBN 9781538628409
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