Object Registration in Semi-cluttered and Partial-occluded Scenes for Augmented Reality.

Gao, Q. H., Wan, T. R., Tang, W. and Chen, L., 2018. Object Registration in Semi-cluttered and Partial-occluded Scenes for Augmented Reality. Multimedia Tools and Applications. (In Press)

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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.

Item Type:Article
ISSN:0942-4962
Uncontrolled Keywords:Augmented Reality; 3D Object Recognition and Matching; 3D Point Clouds; SLAM Algorithm
Group:Faculty of Science & Technology
ID Code:31461
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:16 Nov 2018 16:41
Last Modified:16 Nov 2018 16:50

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