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Object registration in semi-cluttered and partial-occluded scenes for augmented reality

Gao, Q.H.
; 
Wan, Tao Ruan
; 
Tang, W.
; 
Chen, L.
Publication Date
2018-06
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Keywords
Augmented reality, 3D object recognition and matching, 3D point clouds, SLAM algorithm
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© The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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2018-11-14
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Engineering and Digital Technology Publications
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Abstract
This paper proposes a stable and accurate object registration pipeline for markerless augmented reality applications. We present two novel algorithms for object recognition and matching to improve the registration accuracy from model to scene transformation via point cloud fusion. Whilst the first algorithm effectively deals with simple scenes with few object occlusions, the second algorithm handles cluttered scenes with partial occlusions for robust real-time object recognition and matching. The computational framework includes a locally supported Gaussian weight function to enable repeatable detection of 3D descriptors. We apply a bilateral filtering and outlier removal to preserve edges of point cloud and remove some interference points in order to increase matching accuracy. Extensive experiments have been carried to compare the proposed algorithms with four most used methods. Results show improved performance of the algorithms in terms of computational speed, camera tracking and object matching errors in semi-cluttered and partial-occluded scenes.
URI
http://hdl.handle.net/10454/16671
Version
Published version
Citation
Gao QH, Wan TR, Tang W, et al (2018) Object registration in semi-cluttered and partial-occluded scenes for augmented reality. Multimedia Tools and Applications. 78(11): 15079-15099.
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Link to Version of Record
https://doi.org/10.1007/s11042-018-6905-5
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Article
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