In this paper, we describe a shape-based 3D model retrieval method based on multi-scale local visual features. The features are extracted from 2D range images of the model viewed from uniformly sampled locations on a view sphere. The method is appearance-based, and accepts all the models that can be rendered as a range image. For each range image, a set of 2D multi-scale local visual features is computed by using the Scale Invariant Feature Transform [22] algorithm. To reduce cost of distance computation and feature storage, a set of local features describing a 3D model is integrated into a histogram using the Bag-Of-Features approach. Our experiments using two standard benchmarks, one for articulated shapes and the other for rigid shapes, showed that the methods achieved the performance comparable or superior to some of the most powerful 3D shape retrieval methods. KEYWORDS: Content-based retrieval, multi-scale feature, bag-offeatures,

This paper introduces a method to extract fingerprints of any surface or solid object by taking the eigenvalues of its respective LaplaceBeltrami operator. Using an object's spectrum (i.e. the family of its eigenvalues) as a fingerprint for its shape is motivated by the fact that the related eigenvalues are isometry invariants of the object. Employing the Laplace-Beltrami spectra (not the spectra of the mesh Laplacian) as fingerprints of surfaces and solids is a novel approach in the field of geometric modeling and computer graphics. Those spectra can be calculated for any representation of the geometric object (e.g. NURBS or any parametrized or implicitly represented surface or even for polyhedra). Since the spectrum is an isometry invariant of the respective object this fingerprint is also independent of the spatial position. Additionally the eigenvalues can be normalized so that scaling factors for the geometric object can be obtained easily. Therefore checking if two objects are isometric needs no prior alignment (registration/localization) of the objects, but only a comparison of their spectra. With the help of such fingerprints it is possible to support copyright protection, database retrieval and quality assessment of digital data representing surfaces and solids.

Databases of 3D shapes have become widespread for a variety of applications, and a key research problem is searching these databases for similar shapes. This paper introduces a method for finding distinctive features of a shape that are useful for determining shape similarity. Although global shape descriptors have been developed to facilitate retrieval, they fail when local shape properties are the distinctive features of a class. Alternatively, local shape descriptors can be generated over the surface of shapes, but then storage and search of the descriptors becomes unnecessarily expensive, as perhaps only a few descriptors are sufficient to distinguish classes. The challenge is to select local descriptors from a query shape that are most distinctive for retrieval. Our approach is to define distinction as the retrieval performance of a local shape descriptor. During a training phase, we estimate descriptor likelihood using a multivariate Gaussian distribution of real-valued shape descriptors, evaluate the retrieval performance of each descriptor from a training set, and average these performance values at every likelihood value. For each query, we evaluate the likelihood of local shape descriptors on its surface and lookup the expected retrieval values learned from the training set to determine their predicted distinction values. We show that querying with the most distinctive shape descriptors provides favorable retrieval performance during tests with a database of common graphics objects.

Figure 1: Left: A man-made model with unnatural orientation. Middle: Six orientations obtained by aligning the model into a canonical coordinate frame using Principal Component Analysis. Right: Our method automatically detects the upright orientation of the model from its geometry alone. Humans usually associate an upright orientation with objects, placing them in a way that they are most commonly seen in our surroundings. While it is an open challenge to recover the functionality of a shape from its geometry alone, this paper shows that it is often possible to infer its upright orientation by analyzing its geometry. Our key idea is to reduce the two-dimensional (spherical) orientation space to a small set of orientation candidates using functionality-related geometric properties of the object, and then determine the best orientation using an assessment function of several functional geometric attributes defined with respect to each candidate. Specifically we focus on obtaining the upright orientation for man-made objects that typically stand on some flat surface (ground, floor, table, etc.), which include the vast majority of objects in our everyday surroundings. For these types of models orientation candidates can be defined according to static equilibrium. For each candidate, we introduce a set of discriminative attributes linking shape to function. We learn an assessment function of these attributes from a training set using a combination of Random Forest classifier and Support Vector Machine classifier. Experiments demonstrate that our method generalizes well and achieves about 90 % prediction accuracy for both a 10-fold cross-validation over the training set and a validation with an independent test set. 1

3D objects are an important multimedia data type with many applications in domains such as Computer Aided Design, Simulation, Visualization, and Entertainment. Advancements in production, acquisition, and dissemination technology contribute to growing repositories of 3D objects. Consequently, there is a demand for advanced searching and indexing techniques to make effective and efficient use of such large repositories. Methods for automatically extracting descriptors from 3D objects are a key approach to this end. In this paper, we survey techniques for searching for similar content in databases of 3D objects. We address the basic concepts for extraction of 3D object descriptors which in turn can be used for searching and indexing. We sketch the wealth of different descriptors by two recently proposed schemes, and discuss methods for benchmarking the qualitative performance of 3D retrieval systems.

The description of 3D shapes with features that possess descriptive power is one of the most challenging issues in content based 3D model retrieval. In this paper we propose the usage of spherical wavelet transform as a tool for the analysis of 3D shapes represented by functions on the unit sphere. We introduce three new shape descriptors extracted from the spherical wavelet coefficients, namely: (1) a subset of the spherical wavelet coefficients, (2) the L1 and, (3) the L2 energies of the spherical wavelet sub-bands. The advantage of this tool is three fold: First, it filters out small shape details which hamper the retrieval performance. Second, it takes into account feature localization and local orientations. Third, it allows shape matching at different resolutions. Spherical wavelet descriptors are natural extension of 3D Zernike moments and spherical harmonics. We evaluate, on the Princeton Shape Benchmark, the proposed descriptors regarding computational aspects and shape retrieval performance.

Abstract—We address content-based retrieval of complete 3D object models by a probabilistic generative description of local shape properties. The proposed shape description framework characterizes a 3D object with sampled multivariate probability density functions of its local surface features. This density-based descriptor can be efficiently computed via kernel density estimation (KDE) coupled with fast Gauss transform. The nonparametric KDE technique allows reliable characterization of a diverse set of shapes and yields descriptors which remain relatively insensitive to small shape perturbations and mesh resolution. Density-based characterization also induces a permutation property which can be used to guarantee invariance at the shape matching stage. As proven by extensive retrieval experiments on several 3D databases, our framework provides state-of-the-art discrimination over a broad and heterogeneous set of shape categories.

In this paper, we present a semi-supervised semantic clustering method based on Support Vector Machines (SVM) to organize the 3D models semantically. Ground truth data is used to identify the pattern of each semantic category by supervised learning. The unknown data is then automatically classified and clustered based on the resulting pattern. We also propose a unified search strategy which applies semantic constraints to the retrieval by using the resulting clusters. A query is first labeled with its semantic concept therefore shape-based search is only conducted in the corresponding cluster. Experiments are performed to evaluate the effects of the semantic clustering and retrieval respectively by using our prototypical 3D Engineering

Abstract. Distance measures, along with shape features, are the most critical components in a shape-based 3D model retrieval system. Given a shape feature, an optimal distance measure will vary per query, per user, or per database. No single, fixed distance measure would be satisfactory all the time. This paper focuses on a method to adapt distance measure to the database to be queried by using learning-based dimension reduction algorithms. We experimentally compare six such dimension reduction algorithms, both linear and non-linear, for their efficacy in the context of shape-based 3D model retrieval. We tested the efficacy of these methods by applying them to five global shape features. Among the dimension reduction methods we tested, non-linear manifold learning algorithms performed better than the other, e.g. linear algorithms such as principal component analysis. Performance of the best performing combination is roughly the same as the top finisher in the SHREC 2006 contest. 1.

C. Ip and S.K. Gupta. Retrieving matching CAD models by using partial 3D point