TY - JOUR
T1 - Controllable blending of line and polygon skeleton-based convolution surfaces with finite support kernels
AU - Zhu, Xiaoqiang
AU - Chen, Qi
AU - Liu, Sihu
AU - Fan, Chenjie
AU - Song, Chenze
AU - Zhang, Junjie
AU - Zeng, Dan
AU - Jin, Xiaogang
N1 - Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/8
Y1 - 2022/8
N2 - We present a novel approach to control the blending of line and polygon skeleton-based convolution surfaces using locally varying Ratio of Support radius and Thickness (RST). With our method, solutions for local convolution surface approximation with prescribed surface thickness and support radii can be derived analytically. In addition, iso-surface shrinkage can be avoided by offsetting the endpoints of line skeletons and the edges of polygon skeletons. Our RST-based blending for convolution surfaces is local and can generate desired blending effects while approximating shapes with a specified thickness. Moreover, our method is intuitive and users can control the blending by adjusting the skeletal radius or the support radius of the finite support kernel independently. As our blending utilizes convolution integration only without requiring any extra composition operators, it allows for successive convolution blending operations to create complex shapes.
AB - We present a novel approach to control the blending of line and polygon skeleton-based convolution surfaces using locally varying Ratio of Support radius and Thickness (RST). With our method, solutions for local convolution surface approximation with prescribed surface thickness and support radii can be derived analytically. In addition, iso-surface shrinkage can be avoided by offsetting the endpoints of line skeletons and the edges of polygon skeletons. Our RST-based blending for convolution surfaces is local and can generate desired blending effects while approximating shapes with a specified thickness. Moreover, our method is intuitive and users can control the blending by adjusting the skeletal radius or the support radius of the finite support kernel independently. As our blending utilizes convolution integration only without requiring any extra composition operators, it allows for successive convolution blending operations to create complex shapes.
KW - Convolution surfaces
KW - Implicit surface blending
KW - Semi-analytical solutions
KW - Support radius
UR - http://www.scopus.com/inward/record.url?scp=85132210587&partnerID=8YFLogxK
U2 - 10.1016/j.cag.2022.05.016
DO - 10.1016/j.cag.2022.05.016
M3 - Article
AN - SCOPUS:85132210587
SN - 0097-8493
VL - 106
SP - 98
EP - 109
JO - Computers and Graphics (Pergamon)
JF - Computers and Graphics (Pergamon)
ER -