3D shape retrieval using temporal series features

May, 2016| Master Thesis


With the increasing data storage capacity of databases and personal computers, arises the necessity of computer algorithms capable of performing processing for automatic recovery of data and information. This fact is no different for three-dimensional objects stored in files. In this Master’s Thesis we studied new techniques for processing such data objects using an unusual approach to the geometric processing area: techniques for analyzing time series, such as scattering wavelets and recurrence plots. For shape retrieval problem, i.e., given a tridimensional mesh try finding other meshes that are visually similar, our method extract only one feature - Gaussian curvature and surface variation, for example - and organize it as a series using information given by Fiedler vector. Then, the next step is to process the resulting series using a technique called scattering wavelets, that is capable of analyzing the temporal behavior of a set of serial data. For this problem, the results are comparable with other approaches reported in the literature that use multiple characteristics to find a matching mesh. In the case of partial retrieval of objects, in which only a part of the object is given as search parameter, it is necessary to perform a segmentation of the meshes in order to find other parts that are visually similar to the query. By using Recurrence Plot to analyze the objects, our method can find not only the most similar region within the same (or other) object, but also get all the regions that are similar to the search parameter.

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