Efficient Mesh Generation Using Subdivision Surfaces

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Andrés Adolfo Navarro Newball Geoff Wyvill Brendan McCane


Polygonal meshes and particularly triangular meshes are the most used structure for 3D modelling. The ‘di­rect edges’ data structure is the most efficient way to represent them and subdivision surfaces is an appropri­ate method to generate them. From a review of subdivision surfaces we chose the ‘√3 subdivision’ method for mesh generation. Our main challenge was to take advantage of the direct edges data structure and to find the right formulas for an efficient imple­mentation. We decided to use files in the 3DS file format and convert them to the direct edges data structures for use in our application. We tested our algorithm with arbitrary mesh topologies and calculated efficiency. Our implementation will be used in the creation of a virtual dog head.

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Original Research
Author Biographies

Andrés Adolfo Navarro Newball, Pontificia Universidad Javeriana, Cali

Com­puter Scientist from the Universi­dad Javeriana. MSc in Computer Graphics from the University of Hull. Networks Specialist from the ICESI University. Beneficiary of the Coimbra Scholarship at the Universitá degli Studi di Siena. He was part of the Colombian Te­lemedicine Centre. He is lecturer at the ICESI and the Universidad Javeriana where he leads the DESTINO research group. He is a PhD candidate at the University of Otago.

Geoff Wyvill, Uni­versity of Otago

BA in Physics from Oxford, MSc and PhD in Computer Science from Bradford. Professor at the Department of Computer Science at the Uni­versity of Otago. He directs the Computer Graphics lab at the Computer Science Department. Teaching over 30 years, he ac­counts over 100 publications in Computer Graphics and is mem­ber of several editorial boards.

Brendan McCane, Uni­versity of Otago

Received his BSc(Hons) and PhD from James Cook University of North Queensland. He joined the Depar­tment of Computer Science at the University of Otago in 1997 and has been Head of Department since 2007. His research interests include computer vision, pattern recognition, medical imaging, machine learning and computer graphics.