||We propose a novel feature vector suitable for searching
collections of 3D-objects by shape similarity. In this
search a polygonal mesh model serves as a query. For each
model feature vectors are automatically extracted and
stored. Shape similarity between 3D-objects in the search
space is determined by finding and ranking nearest
neighbors in the feature vector space. Ranked objects are
retrieved for inspection, selection, and processing. The
feature vector is obtained by forming a complex function
on the sphere. Afterwards, we apply the Fast Fourier
Transform (FFT) on the sphere and obtain Fourier
coefficients for spherical harmonics. The absolute values
of the coefficients form the feature vector.
Retrieval efficiency of the new approach is evaluated
by constructing precision/recall diagrams and using two
different 3D-model databases. We compared the approach
with two methods based on real functions on the sphere.
Our empirical comparison showed that the complex feature
vector performed best.
We also prepared a Web-based retrieval system for
testing methods discussed in this paper.