Density-Based 3D Shape Descriptors

Ceyhun Burak Akgül,Francis Schmitt,Bülent Sankur,Yücel Yemez

doi:10.1155/2007/32503

Abstract

We propose a novel probabilistic framework for the extraction of density-based 3D shape descriptors using kernel density estimation. Our descriptors are derived from the probability density functions (pdf) of local surface features characterizing the 3D object geometry. Assuming that the shape of the 3D object is represented as a mesh consisting of triangles with arbitrary size and shape, we provide efficient means to approximate the moments of geometric features on a triangle basis. Our framework produces a number of 3D shape descriptors that prove to be quite discriminative in retrieval applications. We test our descriptors and compare them with several other histogram-based methods on two 3D model databases, Princeton Shape Benchmark and Sculpteur, which are fundamentally different in semantic content and mesh quality. Experimental results show that our methodology not only improves the performance of existing descriptors, but also provides a rigorous framework to advance and to test new ones.

Highlights

The use of 3D models is becoming increasingly more commonplace with their distribution on the Internet and with the availability of 3D scanners
Our descriptors are derived from the probability density functions of local surface features characterizing the 3D object geometry
When a query model is presented to the 3D object database, its descriptor is calculated and compared to all the stored descriptors using a distance function

Summary

Introduction

The use of 3D models is becoming increasingly more commonplace with their distribution on the Internet and with the availability of 3D scanners. Efficient organization and access to these databases demand effective tools for indexing, categorization, classification, and representation of 3D objects. All these database activities hinge on the development of 3D object similarity measures. There are two paradigms for 3D object database operations and design of similarity measures, namely, the feature vector approach and the nonfeature vector approach [1, 2]. The feature vector paradigm aims at obtaining numerical values of certain shape descriptors and measuring the distances between these vectors. A typical example of nonfeature-based approach is to describe the object as a graph and use graph similarity metrics. We follow the feature vector paradigm, and we limit our scope to the subclass of histogram-based descriptors

Objectives

Results

Conclusion