Pressing: Smooth isosurfaces with flats from binary grids

We explore the automatic recovery of solids from their volumetric discretizations. In particular, we propose an approach, called Pressing, for smoothing isosurfaces extracted from binary volumes while recovering their large planar regions (flats). Pressing yields a surface that is guaranteed to cont...

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Detalles Bibliográficos
Autores: Chica Calaf, Antonio|||0000-0003-0270-2332, Williams, J., Andújar Gran, Carlos Antonio|||0000-0002-8480-4713, Brunet Crosa, Pere|||0000-0001-8406-1975, Navazo Álvaro, Isabel|||0000-0001-6298-1463, Rossignac, J., Vinacua Pla, Álvaro|||0000-0001-8984-4311
Tipo de recurso: informe técnico
Fecha de publicación:2006
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/85928
Acceso en línea:https://hdl.handle.net/2117/85928
Access Level:acceso abierto
Palabra clave:Isosurface extraction
Isosurface smoothing
Feature extraction
Àrees temàtiques de la UPC::Informàtica::Infografia
Descripción
Sumario:We explore the automatic recovery of solids from their volumetric discretizations. In particular, we propose an approach, called Pressing, for smoothing isosurfaces extracted from binary volumes while recovering their large planar regions (flats). Pressing yields a surface that is guaranteed to contain the samples of the volume classified as interior and exclude those classified as exterior. It uses global optimization to identify flats and constrained bilaplacian smoothing to eliminate sharp features and high-frequencies from the rest of the isosurface. It recovers sharp edges between flat regions and between flat and smooth regions. Hence, the resulting isosurface is usually a much more accurate approximation of the original solid than isosurfaces produced by previously proposed approaches. Furthermore, the segmentation of the isosurface into flat and curved faces and the sharp/smooth labelling of their edges may be valuable for shape recognition, simplification, compression, and various reverse engineering and manufacturing applications.