Affiliations: [a] College of Information Engineering the Attached High School, and Department of Foreign Languages, Northwest A&F University, Yangling, Shaanxi, China | [b] National Centre for Computer Animation, Bournemouth University, Poole, Dorset, UK | [c] Changzhou University, Changzhou, Jiangsu, China
Correspondence:
[*]
Corresponding author: Xin Wang, Department of Foreign Languages, Northwest A&F University, Yangling, Shaanxi, China. E-mail:[email protected]
Abstract: 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.
