Object registration in semi-cluttered and partial-occluded scenes for augmented reality
Authors: Gao, Q.H., Wan, T.R., Tang, W. and Chen, L.
Journal: Multimedia Tools and Applications
Volume: 78
Issue: 11
Pages: 15079-15099
eISSN: 1573-7721
ISSN: 1380-7501
DOI: 10.1007/s11042-018-6905-5
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.
https://eprints.bournemouth.ac.uk/31461/
Source: Scopus
Object Registration in Semi-cluttered and Partial-occluded Scenes for Augmented Reality
Authors: Gao, Q.H., Wan, T.R., Tang, W. and Chen, L.
Journal: Multimedia Tools and Applications
Publisher: Springer Nature
ISSN: 0942-4962
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.
https://eprints.bournemouth.ac.uk/31461/
Source: Manual
Object Registration in Semi-cluttered and Partial-occluded Scenes for Augmented Reality
Authors: Gao, Q.H., Wan, T.R., Tang, W. and Chen, L.
Journal: Multimedia Tools and Applications
Volume: 78
Issue: 11
Pages: 15079-15099
ISSN: 0942-4962
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.
https://eprints.bournemouth.ac.uk/31461/
Source: BURO EPrints