| Abstract |
We present tools for 3D object retrieval in which a model,
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 (pose estimation) in which models are transformed into a canonical
coordinate frame. Second, feature vectors are extracted and compared
with those derived from normalized models in the search space. Using
a metric in the feature vector space nearest neighbors are computed
and ranked. Objects thus retrieved are displayed for inspection, selection,
and processing. For the pose estimation we introduce a modified
Karhunen-Loeve transform that takes into account not only vertices or
polygon centroids from the 3D models but all points in the polygons of
the objects. Some feature vectors can be regarded as samples of functions
on the 2-sphere. We use Fourier expansions of these functions
as uniform representations allowing embedded multi-resolution feature
vectors. Our implementation demonstrates and visualizes these tools. |