3D shape retrieval by Poisson histogram

Authors:
Xiang Pan;Qian You;Zhi Liu;Qi Hua Chen
Affiliations:
College of Computer, Zhejiang University of Technology, 310014 Hangzhou, PR China;Department of Computer and Information Science, Purdue School of Science, 723 W. Michigan Street, SL280, Indianapolis, IN 46202, USA;College of Computer, Zhejiang University of Technology, 310014 Hangzhou, PR China;College of Mechanical, Zhejiang University of Technology, 310014 Hangzhou, PR China
Venue:
Pattern Recognition Letters
Year:
2011

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Abstract

Defining a compact and robust shape descriptor is the most important issue in 3D shape retrieval. This paper proposes a new shape descriptor called Poisson histogram, which can capture shape structure feature very well. Moreover, it is robust under different geometry processing and has low feature dimension for efficient indexing. Poisson histogram can be defined by the following two steps. Firstly, we use Poisson equation to define a 3D shape signature. Secondly, we derive a histogram-based shape descriptor by accumulating the values of the defined signature in bins. To verify the performance of Poisson histogram, we perform an experimental comparison on McGill database of 3D shapes. Results show that Poisson histogram is better than several existing histogram-based shape descriptors both in retrieving accuracy and retrieving efficiency.