A Shape-Based Classifier of Domain-Independent 3D Models

Abstract

This paper proposes D2a, a new 3D model classifier that extends the D2 classifier from Osada et al. of Princeton University. The probability distribution of the ratio of areas of faces containing two random points from a 3D model is stored as the second dimension of a 2D array; while the first dimension contains the frequency distribution of distances of randomly generated point pairs (the D2 distribution). The resulting descriptor, D2a, is a two-dimensional histogram that incorporates these two shape features. The results of the classifier using four bins for the D2a descriptor are presented. The effectiveness is tested using the Princeton Shape Benchmark which shows an improvement over the original D2 classifier in several performance metrics.