A comparison of methods for non-rigid 3D shape retrieval

Abstract

Artículo de publicación ISI.Non-rigid 3D shape retrieval has become an active and important research topic in content-based 3D object retrieval. The aim of this paper is to measure and compare the performance of state-of-the-art methods for non-rigid 3D shape retrieval. The paper develops a new benchmark consisting of 600 non-rigid 3D watertight meshes, which are equally classified into 30 categories, to carry out experiments for 11 different algorithms, whose retrieval accuracies are evaluated using six commonly utilized measures. Models and evaluation tools of the new benchmark are publicly available on our web site [1]. (C) 2012 Elsevier Ltd. All rights reservedChina Postdoctoral Science Foundation (GrantNo.2012M510274),the SIMA program, the Shape Metrology IMS, and Fondecyt (Chile) Project 111011

Retrieval and classification methods for textured 3D models: a comparative study

“…To deal with non-rigid deformations (bendings) it is necessary to adopt shape descriptions that are invariant to isometric shape deformations. A suitable metric for comparing non-rigid shapes is the geodesic one; indeed 3D shape descriptions based on geodesics, such as geodesic distance matrices [68] or geodesic skeleton paths [38], have been successfully adopted for non-rigid shape comparison, see also [44]. In addition to geodesic, more sophisticated choices are possible, such as the diffusion or the commute-time distance [77].…”

Matching 3D CAD Assembly Models with Different Layouts of Components Using Projections

“…A large number of methods for retrieving rigid or non-rigid 3D models have been proposed [4], [6]. However there are not many studies on retrieving 3D CAD assembly models.…”

Multiscale Symmetry Detection in Scalar Fields by Clustering Contours

“…Shape descriptors have been extensively used in the geometry processing community for shape matching and there is a vast collection of research papers in this area [15,26,37,40]. It is possible that a shape descriptor may incorrectly map contours c 1 and c 2 to the same point even when they have totally different shapes.…”

“…The only prerequisite on the descriptor is that it should be discriminative, i.e., similar contours should be mapped to points that are nearby in the descriptor space while contours that differ from each other should be mapped to far away points. Therefore, it is important to use shape descriptors with high precision and recall ratios [15] for applications that cannot tolerate false positives during shape retrieval.…”

“…A function φ which satisfies the equation ∆φ = −λ φ , where ∆ is the Laplace-Beltrami operator, is called an eigen function of ∆ and λ is called an eigen value [30]. We use the first ten non-zero eigen values of the Laplace-Beltrami spectra as the shape descriptor since noise in the shape has limited influence on the initial eigen values [15]. This descriptor has been used for shape matching and retrieval and is robust in the presence of noise, deformations in shape, and differences in the underlying triangulation [24,28,29].…”