Yes / 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. / Shanxi Natural Science and Technology Foundation of China, grant number 2016JZ026 and grant number 2016KW-043).
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/16671 |
| Date | 26 November 2018 |
| Creators | Gao, Q.H., Wan, Tao Ruan, Tang, W., Chen, L. |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Article, Published version |
| Rights | © 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. |
Page generated in 0.0025 seconds