Polyhedral model retrieval using weighted point sets

Tangelder, Johan W. H.; Veltkamp, Remco C.

doi:10.1109/smi.2003.1199608

Cited by 43 publications

(21 citation statements)

References 26 publications

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“…3.4.8 Point sets methods. The weighted point set method [Tangelder and Veltkamp 2003] compares two 3D objects represented by polyhedral meshes. A shape signature of the 3D object is defined as a set of points that consists of weighted salient points from the object.…”

Section: Volume-based Descriptorsmentioning

confidence: 99%

Feature-based similarity search in 3D object databases

et al. 2005

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The development of effective content-based 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 acquisition and dissemination technology of 3D models is constantly improving. 3D objects are becoming an important type of multimedia data, with many promising application possibilities. Defining the aspects that constitute the similarity among 3D objects, and designing algorithms that implement such similarity definitions, is a difficult problem. Over the last few years, a strong interest in methods for 3D similarity search has arisen, and a growing number of competing algorithms for content-based retrieval of 3D objects have been proposed. We survey feature-based methods for 3D retrieval, and we propose a taxonomy for these methods. We also present experimental results, comparing the effectiveness of some of the surveyed methods.

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Section: Volume-based Descriptorsmentioning

confidence: 99%

Feature-based similarity search in 3D object databases

et al. 2005

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“…This is a hard to do experiment and still outstanding. For a more detailed discussion see [2,11,16,17].…”

Section: Invariance Requirements and The Principal Component Analysismentioning

confidence: 99%

An experimental effectiveness comparison of methods for 3D similarity search

et al. 2006

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Methods for content-based similarity search are fundamental for managing large multimedia repositories, as they make it possible to conduct queries for similar content, and to organize the repositories into classes of similar objects. 3D objects are an important type of multimedia data with many promising application possibilities. Defining the aspects that constitute the similarity among 3D objects, and designing algorithms that implement such similarity definitions is a difficult problem. Over the last few years, a strong interest in 3D similarity search has arisen, and a growing number of competing algorithms for the retrieval of 3D objects have been proposed. The contributions of this paper are to survey a body of recently proposed methods for 3D similarity search, to organize them along a descriptor extraction process model, and to present an extensive experimental effectiveness and efficiency evaluation of these methods, using several 3D databases.

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“…Graph-based [29,30] 3. Geometry-based [31][32][33][34][35][36][37][38] Fig. 1 Taxonomical classification of shape analysis techniques.…”

Section: Introductionmentioning

confidence: 99%

“…In the first step, the shape is decomposed into its components, and then each component is represented using a weighted point [37]. Curvature of points for example can be a very good measure for weighing [38].…”

Section: Introductionmentioning

confidence: 99%

3D shape representation with spatial probabilistic distribution of intrinsic shape keypoints

Ghorpade

Checchin

Laurent

et al. 2017

EURASIP J. Adv. Signal Process.

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The accelerated advancement in modeling, digitizing, and visualizing techniques for 3D shapes has led to an increasing amount of 3D models creation and usage, thanks to the 3D sensors which are readily available and easy to utilize. As a result, determining the similarity between 3D shapes has become consequential and is a fundamental task in shape-based recognition, retrieval, clustering, and classification. Several decades of research in Content-Based Information Retrieval (CBIR) has resulted in diverse techniques for 2D and 3D shape or object classification/retrieval and many benchmark data sets. In this article, a novel technique for 3D shape representation and object classification has been proposed based on analyses of spatial, geometric distributions of 3D keypoints. These distributions capture the intrinsic geometric structure of 3D objects. The result of the approach is a probability distribution function (PDF) produced from spatial disposition of 3D keypoints, keypoints which are stable on object surface and invariant to pose changes. Each class/instance of an object can be uniquely represented by a PDF. This shape representation is robust yet with a simple idea, easy to implement but fast enough to compute. Both Euclidean and topological space on object's surface are considered to build the PDFs. Topology-based geodesic distances between keypoints exploit the non-planar surface properties of the object. The performance of the novel shape signature is tested with object classification accuracy. The classification efficacy of the new shape analysis method is evaluated on a new dataset acquired with a Time-of-Flight camera, and also, a comparative evaluation on a standard benchmark dataset with state-of-the-art methods is performed. Experimental results demonstrate superior classification performance of the new approach on RGB-D dataset and depth data.

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Polyhedral model retrieval using weighted point sets

Cited by 43 publications

References 26 publications

Feature-based similarity search in 3D object databases

Feature-based similarity search in 3D object databases

An experimental effectiveness comparison of methods for 3D similarity search

3D shape representation with spatial probabilistic distribution of intrinsic shape keypoints

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