Fitting Undeformed Superquadrics to Range Data: Improving Model Recovery and Classification
نویسندگان
چکیده
Undeformed superquadrics are volumetric modeling primitives with an extensive shape vocabulary that are described by only 5 parameters. Fitting these models viewpoint invariantly to range data enables classi cation based on the superquadric parameters. However, current recovery routines show several limitations, especially when the algorithms are applied to range images instead of true 3D images. In this paper problems with the common superquadric recovery procedure are identi ed and solutions are presented.
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تاریخ انتشار 1998