Results 1  10
of
179
A search engine for 3d models
 ACM Transactions on Graphics
, 2003
"... As the number of 3D models available on the Web grows, there is an increasing need for a search engine to help people find them. Unfortunately, traditional textbased search techniques are not always effective for 3D data. In this paper, we investigate new shapebased search methods. The key challen ..."
Abstract

Cited by 318 (22 self)
 Add to MetaCart
As the number of 3D models available on the Web grows, there is an increasing need for a search engine to help people find them. Unfortunately, traditional textbased search techniques are not always effective for 3D data. In this paper, we investigate new shapebased search methods. The key challenges are to develop query methods simple enough for novice users and matching algorithms robust enough to work for arbitrary polygonal models. We present a webbased search engine system that supports queries based on 3D sketches, 2D sketches, 3D
A survey of content based 3D shape retrieval methods
 Multimedia Tools and Applications
, 2008
"... Recent developments in techniques for modeling, digitizing and visualizing 3D shapes has led to an explosion in the number of available 3D models on the Internet and in domainspecific databases. This has led to the development of 3D shape retrieval systems that, given a query object, retrieve simil ..."
Abstract

Cited by 289 (1 self)
 Add to MetaCart
(Show Context)
Recent developments in techniques for modeling, digitizing and visualizing 3D shapes has led to an explosion in the number of available 3D models on the Internet and in domainspecific databases. This has led to the development of 3D shape retrieval systems that, given a query object, retrieve similar 3D objects. For visualization, 3D shapes are often represented as a surface, in particular polygonal meshes, for example in VRML format. Often these models contain holes, intersecting polygons, are not manifold, and do not enclose a volume unambiguously. On the contrary, 3D volume models, such as solid models produced by CAD systems, or voxels models, enclose a volume properly. This paper surveys the literature on methods for content based 3D retrieval, taking into account the applicability to surface models as well as to volume models. The methods are evaluated with respect to several requirements of content based 3D shape retrieval, such as: (1) shape representation requirements, (2) properties of dissimilarity measures, (3) efficiency, (4) discrimination abilities, (5) ability to perform partial matching, (6) robustness, and (7) necessity of pose normalization. Finally, the advantages and limits of the several approaches in content based 3D shape retrieval are discussed. 1.
Rotation Invariant Spherical Harmonic Representation of 3D Shape Descriptors
, 2003
"... One of the challenges in 3D shape matching arises from the fact that in many applications, models should be considered to be the same if they differ by a rotation. Consequently, when comparing two models, a similarity metric implicitly provides the measure of similarity at the optimal alignment. E ..."
Abstract

Cited by 285 (11 self)
 Add to MetaCart
One of the challenges in 3D shape matching arises from the fact that in many applications, models should be considered to be the same if they differ by a rotation. Consequently, when comparing two models, a similarity metric implicitly provides the measure of similarity at the optimal alignment. Explicitly solving for the optimal alignment is usually impractical. So, two general methods have been proposed for addressing this issue: (1) Every model is represented using rotation invariant descriptors. (2) Every model is described by a rotation dependent descriptor that is aligned into a canonical coordinate system defined by the model. In this paper, we discuss the limitations of canonical alignment and present a new mathematical tool, based on spherical harmonics, for obtaining rotation invariant representations. We describe the properties of this tool and show how it can be applied to a number of existing, orientation dependent, descriptors to improve their matching performance. The advantage of this is twofold: First, it improves the matching performance of many descriptors. Second, it reduces the dimensionality of the descriptor, providing a more compact representation, which in turn makes comparing two models more efficient.
On Visual Similarity Based 3D Model Retrieval
, 2003
"... A large number of 3D models are created and available on the Web, since more and more 3D modelling and digitizing tools are developed for ever increasing applications. The techniques for contentbased 3D model retrieval then become necessary. In this paper, a visual similaritybased 3D model retriev ..."
Abstract

Cited by 197 (4 self)
 Add to MetaCart
A large number of 3D models are created and available on the Web, since more and more 3D modelling and digitizing tools are developed for ever increasing applications. The techniques for contentbased 3D model retrieval then become necessary. In this paper, a visual similaritybased 3D model retrieval system is proposed.
Properties of embedding methods for similarity searching in metric spaces
 PAMI
, 2003
"... Complex data types—such as images, documents, DNA sequences, etc.—are becoming increasingly important in modern database applications. A typical query in many of these applications seeks to find objects that are similar to some target object, where (dis)similarity is defined by some distance functi ..."
Abstract

Cited by 109 (5 self)
 Add to MetaCart
Complex data types—such as images, documents, DNA sequences, etc.—are becoming increasingly important in modern database applications. A typical query in many of these applications seeks to find objects that are similar to some target object, where (dis)similarity is defined by some distance function. Often, the cost of evaluating the distance between two objects is very high. Thus, the number of distance evaluations should be kept at a minimum, while (ideally) maintaining the quality of the result. One way to approach this goal is to embed the data objects in a vector space so that the distances of the embedded objects approximates the actual distances. Thus, queries can be performed (for the most part) on the embedded objects. In this paper, we are especially interested in examining the issue of whether or not the embedding methods will ensure that no relevant objects are left out (i.e., there are no false dismissals and, hence, the correct result is reported). Particular attention is paid to the SparseMap, FastMap, and MetricMap embedding methods. SparseMap is a variant of Lipschitz embeddings, while FastMap and MetricMap are inspired by dimension reduction methods for Euclidean spaces (using KLT or the related PCA and SVD). We show that, in general, none of these embedding methods guarantee that queries on the embedded objects have no false dismissals, while also demonstrating the limited cases in which the guarantee does hold. Moreover, we describe a variant of SparseMap that allows queries with no false dismissals. In addition, we show that with FastMap and MetricMap, the distances of the embedded objects can be much greater than the actual distances. This makes it impossible (or at least impractical) to modify FastMap and MetricMap to guarantee no false dismissals.
3D Zernike Descriptors for Content Based Shape Retrieval
 In The 8th ACM Symposium on Solid Modeling and Applications
, 2003
"... Content based 3D shape retrieval for broad domains like the World Wide Web has recently gained considerable attention in Computer Graphics community. One of the main challenges in this context is the mapping of 3D objects into compact canonical representations referred to as descriptors, which serve ..."
Abstract

Cited by 102 (1 self)
 Add to MetaCart
Content based 3D shape retrieval for broad domains like the World Wide Web has recently gained considerable attention in Computer Graphics community. One of the main challenges in this context is the mapping of 3D objects into compact canonical representations referred to as descriptors, which serve as search keys during the retrieval process. The descriptors should have certain desirable properties like invariance under scaling, rotation and translation. Very importantly, they should possess descriptive power providing a basis for similarity measure between threedimensional objects which is close to the human notion of resemblance. In this paper we advocate the usage of socalled 3D Zernike invariants as descriptors for content based 3D shape retrieval. The basis polynomials of this representation facilitate computation of invariants under the above transformations. Some theoretical results have already been summarized in the past from the aspect of pattern recognition and shape analysis. We provide practical analysis of these invariants along with algorithms and computational details. Furthermore, we give a detailed discussion on influence of the algorithm parameters like type and resolution of the conversion into a volumetric function, number of utilized coefficients, etc. As is revealed by our study, the 3D Zernike descriptors are natural extensions of spherical harmonics based descriptors, which are reported to be among the most successful representations at present. We conduct a comparison of 3D Zernike descriptors against these regarding computational aspects and shape retrieval performance.
ThreeDimensional Shape Searching: StateoftheArt Review and Future Trends
 ComputerAided Design
, 2005
"... future trends ..."
(Show Context)
3D Model Retrieval with Spherical Harmonics and Moments
 Eds., Proceedings of the 23rd DAGMSympo sium on Pattern Recognition
, 2001
"... Abstract. We consider 3D object retrieval in which a polygonal mesh serves as a query and similar objects are retrieved from a collection of 3D objects. Algorithms proceed first by a normalization step in which models are transformed into canonical coordinates. Second, feature vectors are extracted ..."
Abstract

Cited by 99 (5 self)
 Add to MetaCart
(Show Context)
Abstract. We consider 3D object retrieval in which a polygonal mesh serves as a query and similar objects are retrieved from a collection of 3D objects. Algorithms proceed first by a normalization step in which models are transformed into canonical coordinates. Second, feature vectors are extracted and compared with those derived from normalized models in the search space. In the feature vector space nearest neighbors are computed and ranked. Retrieved objects are displayed for inspection, selection, and processing. Our feature vectors are based on rays cast from the center of mass of the object. For each ray the object extent in the ray direction yields a sample of a function on the sphere. We compared two kinds of representations of this function, namely spherical harmonics and moments. Our empirical comparison using precisionrecall diagrams for retrieval results in a data base of 3D models showed that the method using spherical harmonics performed better. 1
Featurebased similarity search in 3D object databases
 ACM Computing Surveys
, 2005
"... The development of effective contentbased multimedia search systems is an important research issue due to the growing amount of digital audiovisual information. In the case of images and video, the growth of digital data has been observed since the introduction of 2D capture devices. A similar dev ..."
Abstract

Cited by 84 (10 self)
 Add to MetaCart
The development of effective contentbased multimedia search systems is an important research issue due to the growing amount of digital audiovisual information. In the case of images and video, the growth of digital data has been observed since the introduction of 2D capture devices. A similar development is expected for 3D data as
A Reflective Symmetry Descriptor for 3D Models
 ALGORITHMICA
, 2004
"... Computing reflective symmetries of 2D and 3D shapes is a classical problem in computer vision and computational geometry. Most prior work has focused on finding the main axes of symmetry, or determining that none exists. In this paper we introduce a new reflective symmetry descriptor that represent ..."
Abstract

Cited by 71 (8 self)
 Add to MetaCart
Computing reflective symmetries of 2D and 3D shapes is a classical problem in computer vision and computational geometry. Most prior work has focused on finding the main axes of symmetry, or determining that none exists. In this paper we introduce a new reflective symmetry descriptor that represents a measure of reflective symmetry for an arbitrary 3D model for all planes through the model’s center of mass (even if they are not planes of symmetry). The main benefits of this new shape descriptor are that it is defined over a canonical parameterization (the sphere) and describes global properties of a 3D shape. We show how to obtain a voxel grid from arbitrary 3D shapes and, using Fourier methods, we present an algorithm that computes the symmetry descriptor in O(N 4 log N) time for an N × N × N voxel grid and computes a multiresolution approximation in O(N 3 log N) time. In our initial experiments, we have found that the symmetry descriptor is insensitive to noise and stable under point sampling. We have also found that it performs well in shape matching tasks, providing a measure of shape similarity that is orthogonal to existing methods.