@INPROCEEDINGS{Gumhold-2004-HSAQFGC,
   AUTHOR = {Stefan Gumhold},
   TITLE = {Hierarchical Shape-Adaptive Quantization for Geometry Compression},
   AFFILIATIONS = {CGV,MPI},
   AREAS = {GP},
   BOOKTITLE = {Proceedings of International Workshop on Vision, Modeling, and Visualization},
   URL = {http://tu-dresden.de/die_tu_dresden/fakultaeten/fakultaet_informatik/smt/cgv/publikationen/2004/hsaqfgc/hsaqfgc.pdf},
   MONTH = {nov},
   PAGES = {293--298},
   YEAR = {2004},
   ABSTRACT = {The compression of polygonal mesh geometry is still an active field of research as in 
   3d no theoretical bounds are known. This work proposes a geometry coding method based on predictive 
   coding. Instead of using the vertex to vertex distance as distortion measurement, an approximation 
   to the Hausdorffdistance is used resulting in additional degrees of freedom. These are exploited by 
   a new adaptive quantization approach, which is independent of the encoding order. The achieved 
   compression rates are similar to those of entropy based optimization but with a significantly 
   faster compression performance.},
   ISBN = {3-89838-058-0}
}

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