| Abstract |
We consider 3D object retrieval in which a polygonal mesh
serves as a query and similar objects are retrieved from a collection of 3D
objects. Algorithms proceed first by a normalization step in which models
are transformed into canonical coordinates. Second, feature vectors
are extracted and compared with those derived from normalized models
in the search space. In the feature vector space nearest neighbors are
computed and ranked. Retrieved objects are displayed for inspection, selection,
and processing. Our feature vectors are based on rays cast from
the center of mass of the object. For each ray the object extent in the
ray direction yields a sample of a function on the sphere. We compared
two kinds of representations of this function, namely spherical harmonics
and moments. Our empirical comparison using precision-recall diagrams
for retrieval results in a data base of 3D models showed that the method
using spherical harmonics performed better. |