TY - JOUR
T1 - Tetrahedral meshing in the wild
AU - Hu, Yixin
AU - Zhou, Qingnan
AU - Gao, Xifeng
AU - Jacobson, Alec
AU - Zorin, Denis
AU - Panozzo, Daniele
N1 - Funding Information:
We are grateful to Jérémie Dumas for illuminating discussions, to Wenzel Jakob for the Mitsuba renderer, and to Thingiverse and the Stanford 3D Scanning Repository for the datasets. This work was supported in part by NSF CAREER award 1652515, NSF grant ?IIS-1320635 ? DMS-1436591), NSERC Discovery Grants ?RGPIN-2017-05235 ? RGPAS-2017-507938), Canada Research Chair award, Connaught Fund, and a gift from Adobe Systems and nTopology Inc..
Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018
Y1 - 2018
N2 - We propose a novel tetrahedral meshing technique that is unconditionally robust, requires no user interaction, and can directly convert a triangle soup into an analysis-ready volumetric mesh. The approach is based on several core principles: (1) initial mesh construction based on a fully robust, yet efficient, filtered exact computation (2) explicit (automatic or user-defined) tolerancing of the mesh relative to the surface input (3) iterative mesh improvement with guarantees, at every step, of the output validity. The quality of the resulting mesh is a direct function of the target mesh size and allowed tolerance: Increasing allowed deviation from the initial mesh and decreasing the target edge length both lead to higher mesh quality. Our approach enables "black-box" analysis, i.e. it allows to automatically solve partial differential equations on geometrical models available in the wild, offering a robustness and reliability comparable to, e.g., image processing algorithms, opening the door to automatic, large scale processing of real-world geometric data.
AB - We propose a novel tetrahedral meshing technique that is unconditionally robust, requires no user interaction, and can directly convert a triangle soup into an analysis-ready volumetric mesh. The approach is based on several core principles: (1) initial mesh construction based on a fully robust, yet efficient, filtered exact computation (2) explicit (automatic or user-defined) tolerancing of the mesh relative to the surface input (3) iterative mesh improvement with guarantees, at every step, of the output validity. The quality of the resulting mesh is a direct function of the target mesh size and allowed tolerance: Increasing allowed deviation from the initial mesh and decreasing the target edge length both lead to higher mesh quality. Our approach enables "black-box" analysis, i.e. it allows to automatically solve partial differential equations on geometrical models available in the wild, offering a robustness and reliability comparable to, e.g., image processing algorithms, opening the door to automatic, large scale processing of real-world geometric data.
KW - Mesh Generation
KW - Robust Geometry Processing
KW - Tetrahedral Meshing
UR - http://www.scopus.com/inward/record.url?scp=85055416281&partnerID=8YFLogxK
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U2 - 10.1145/3197517.3201353
DO - 10.1145/3197517.3201353
M3 - Article
AN - SCOPUS:85055416281
SN - 0730-0301
VL - 37
JO - ACM Transactions on Graphics
JF - ACM Transactions on Graphics
IS - 4
M1 - A21
ER -