Share Email Print

Proceedings Paper • new

Local feature entropy based non-uniform simplification algorithm
Author(s): Xiaoli Chu; Yan Zhang
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Aiming to solve the issue that the accuracy and efficiency after the simplification of 3D model is difficult to be balanced, a new method of simplified algorithm based on half-edge collapse nonhomogeneous mesh method of local characteristic entropy is proposed. Detect clustering local area twice. Firstly, detect edge clustering local area where there is 3D data point to obtain normal vector in the area; secondly, detect the normal vector of secondary regional clustering area by the constraints of the center of gravity of the region near 3D data points. According to the definition of information entropy, take the local area characteristic entropy constructed by angle information between the two normal vectors from the two detection method as the half edge collapse cost. The bigger the local area characteristic entropy is, the flatter the region tends to be, and the priority of simplification shall be given to this, otherwise it shall be retented. Lastly, retain the triangle regularity in the simplified mesh judged by the interior angles to reduce the deformation caused by the error. The experimental results show that the algorithm can achieve a better balance in the accuracy and time efficiency of the local details.

Paper Details

Date Published: 9 August 2018
PDF: 8 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 1080670 (9 August 2018); doi: 10.1117/12.2503529
Show Author Affiliations
Xiaoli Chu, Guangdong AIB Polytechnic College (China)
Yan Zhang, Guangdong Univ. of Finance and Economics (China)

Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

© SPIE. Terms of Use
Back to Top