W Fang
Automatic 3D model acquisition for unknown objects based on hybrid vision technology
Fang, W; Zheng, L.Y.; He, B.; Wang, Q.
Abstract
Three-dimensional (3D) model acquisition is the process of building a 3D model of an object. But due to the limited field of view of the scanner, this task is mainly performed by taking several scans with human intervention. In order to make the 3D modeling process efficient, a novel automatic 3D modeling method for unknown objects based on hybrid vision technology in a binocular structured light system (BSLS) is proposed. Firstly, the limit visual vacuums of the BSLS are established, and they will be used to predict the unknown area with an acquired 2.5D range image. With the 2D intensity image acquired synchronously, the coarse boundary size is recovered from Shape from Shading, and it leads the prediction of the unknown area to be more precise. Based on the combination of the predicted contours, the next best viewpoint is determined with more unknown areas visible. The proposed method can be used to obtain the 3D models of unknown objects automatically, and the experimental results illustrate the validity and efficiency of our approach.
Citation
Fang, W., Zheng, L., He, B., & Wang, Q. (2017). Automatic 3D model acquisition for unknown objects based on hybrid vision technology. International Journal of Precision Engineering and Manufacturing, 18(3), 275-284. https://doi.org/10.1007/s12541-017-0035-2
| Journal Article Type | Article |
|---|---|
| Acceptance Date | Nov 29, 2016 |
| Online Publication Date | Mar 9, 2017 |
| Publication Date | Mar 9, 2017 |
| Deposit Date | Nov 29, 2016 |
| Publicly Available Date | Mar 9, 2018 |
| Journal | International Journal of Precision Engineering and Manufacturing |
| Print ISSN | 2234-7593 |
| Electronic ISSN | 2005-4602 |
| Publisher | Springer |
| Peer Reviewed | Peer Reviewed |
| Volume | 18 |
| Issue | 3 |
| Pages | 275-284 |
| DOI | https://doi.org/10.1007/s12541-017-0035-2 |
Files
Accepted Journal Article
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Copyright Statement
The final publication is available at Springer via https://doi.org/10.1007/s12541-017-0035-2
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