Automatic mesh optimization algorithms suffer from the problem that humans are not uniformly sensitive to changes on different parts of the body. This is a problem because when a mesh optimization algorithm typically measures errors caused by triangle reductions, the errors are strictly geometrical, and an error of a certain magnitude on the thigh of a 3D model will be perceived by a human as less of an error than one of equal geometrical significance introduced on the face. The partial solution to this problem proposed in this paper consists of detecting the faces of the 3D assets to be optimized using conventional, existing 2D face detection algorithms, and then using this information to selectively and automatically preserve the faces of 3D assets that are to be optimized, leading to a smaller perceived error in the optimized model, albeit not necessarily a smaller geometrical error. This is done by generating a set of per-vertex weights that are used to scale the errors measured by the reduction algorithm, hence preserving areas with higher weights. The final optimized meshes produced by using this method is found to be subjectively closer to the original 3D asset than their non-weighed counterparts, and if the input meshes conform to certain criteria this method is well suited for inclusion in a fully automatic mesh decimation pipeline
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-119967 |
| Date | January 2015 |
| Creators | Henriksson, Johan |
| Publisher | Linköpings universitet, Medie- och Informationsteknik, Linköpings universitet, Tekniska högskolan |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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