High-efficiency texture coding and synthesis on point-based pear surface

This data was imported from Scopus:

Authors: Yang, H.J., Wang, Z.Y., Wang, X., Chang, J., Xu, J., Zhang, J.F., Chen, C. and Zhang, J.J.

http://eprints.bournemouth.ac.uk/29681/

Journal: Journal of Computational Methods in Sciences and Engineering

Volume: 17

Issue: 3

Pages: 397-409

ISSN: 1472-7978

DOI: 10.3233/JCM-170725

© 2017 IOS Press and the authors. The fruit images on points cloud acquired by the current 3D scanner from field will appear a visible seams, inconvenient data acquisition or taking large space due to unorganized background. We give a SAOW method to cope with the space efficiency and realistic effects of texture synthesis on pear point model. At first, a point-quadtree is proposed to simplify the pear image division. Then, an adaptive multi-granularity morton coding scheme are presented to optimizing the memory space of pear image. At last, weighted oversampling mixing method is mainly focused on texture quality of pear surface. As shown in the experiment results, our adaptive division makes the memory space decline dramatically about 90.7% than non-division and 92.9% than general division respectively; adaptive code scheme helps to reduce the memory to 72.1% of ordinary morton code; weighted oversampling keeps the mixed texture more real and smoothly than current methods.

This data was imported from Web of Science (Lite):

Authors: Yang, H.J., Wang, Z.Y., Wang, X., Chang, J., Xu, J., Zhang, J.F., Chen, C. and Zhang, J.J.

http://eprints.bournemouth.ac.uk/29681/

Journal: JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING

Volume: 17

Issue: 3

Pages: 397-409

eISSN: 1875-8983

ISSN: 1472-7978

DOI: 10.3233/JCM-170725

The data on this page was last updated at 04:59 on August 21, 2018.