Canonical Forms for Non-Rigid 3D Shape Retrieval

Abstract

We present a new benchmark for testing algorithms that create canonical forms for use in non-rigid 3D shape retrieval. We have combined two existing datasets to create a varied collection of models for testing. Canonical forms attempt to factor out a shape's pose, giving a pose-neutral shape. This opens up the possibility of using methods originally designed for rigid retrieval for the task of non-rigid shape retrieval. We demonstrate the benchmark by using it to compare the performance of nine canonical form methods, using three different retrieval algorithms.