caa a22 4500 605541361 CHVBK 20210128100916.0 cr unu---uuuuu 210128e20150201xx s 000 0 eng 10.1007/s00371-014-0924-7 doi (NATIONALLICENCE)springer-10.1007/s00371-014-0924-7 GPU-based polygonization and optimization for implicit surfaces [Elektronische Daten] [Junjie Chen, Xiaogang Jin, Zhigang Deng] Despite the popularity of polygonization of implicit surfaces in graphics applications, an efficient solution to both polygonize and optimize meshes from implicit surfaces on modern GPUs has not been developed to date. In this paper, we introduce a practical GPU-based approach to efficiently polygonize and optimize iso-surface meshes for implicit surfaces. Specifically, we design new schemes to maximally exploit the parallel features of the GPU hardware, by optimizing both the geometry (vertex position, vertex distribution, triangle shape, and triangle normal) and the topology (connectivity) aspects of a mesh. Our experimental results show that, besides significant improvement on the resultant mesh quality, our GPU-based approach is approximately an order of magnitude faster than its CPU counterpart and faster than or comparable to other GPU iso-surface extraction methods. Furthermore, the achieved speedup becomes even higher if the resolution of the iso-surface is increased. Springer-Verlag Berlin Heidelberg, 2014 Implicit surface nationallicence Polygonization nationallicence Mesh optimization nationallicence GPU parallelization nationallicence Chen Junjie State Key Laboratory of CAD&CG, Zhejiang University, Hangzhou, People's Republic of China aut Jin Xiaogang State Key Laboratory of CAD&CG, Zhejiang University, 310058, Hangzhou, People's Republic of China aut Deng Zhigang Computer Science Department, University of Houston, Houston, USA aut The Visual Computer Springer Berlin Heidelberg 31/2(2015-02-01), 119-130 0178-2789 31:2<119 2015 31 371 https://doi.org/10.1007/s00371-014-0924-7 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00371-014-0924-7 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Chen Junjie State Key Laboratory of CAD&CG, Zhejiang University, Hangzhou, People's Republic of China aut NATIONALLICENCE 700 1- Jin Xiaogang State Key Laboratory of CAD&CG, Zhejiang University, 310058, Hangzhou, People's Republic of China aut NATIONALLICENCE 700 1- Deng Zhigang Computer Science Department, University of Houston, Houston, USA aut NATIONALLICENCE 773 0- The Visual Computer Springer Berlin Heidelberg 31/2(2015-02-01), 119-130 0178-2789 31:2<119 2015 31 371