Nefertiti: a query by content software for three-dimensional models databases management

Paquet, Éric; Rioux, Marc

doi:10.1109/im.1997.603886

Cited by 55 publications

(46 citation statements)

References 7 publications

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“…In the previous works on 3D shape descriptors, the idea of gathering and accumulating local surface information is implemented by histograms [1,2,4,5,6,7,8]. Paquet et al use the cord length and the angles between a cord and the principal axes as geometric features to construct univariate histograms [4].…”

Section: Introductionmentioning

confidence: 99%

Density-Based Shape Descriptors for 3D Object Retrieval

Akgül

Sankur

Schmitt

et al. 2006

Multimedia Content Representation, Classification and Security

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Abstract. We develop a probabilistic framework that computes 3D shape descriptors in a more rigorous and accurate manner than usual histogram-based methods for the purpose of 3D object retrieval. We first use a numerical analytical approach to extract the shape information from each mesh triangle in a better way than the sparse sampling approach. These measurements are then combined to build a probability density descriptor via kernel density estimation techniques, with a rule-based bandwidth assignment. Finally, we explore descriptor fusion schemes. Our analytical approach reveals the true potential of densitybased descriptors, one of its representatives reaching the top ranking position among competing methods.

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Section: Introductionmentioning

confidence: 99%

Density-Based Shape Descriptors for 3D Object Retrieval

Akgül

Sankur

Schmitt

et al. 2006

Multimedia Content Representation, Classification and Security

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“…3-D model capture is an important element and one of the partners, GET-ENST, has developed techniques for accurately generating 3-D models from multiple views [19]. Various authors have published algorithms for 3-D model matching using a variety of feature vectors extracted from mesh based 3-D representations based on for example, 3-D Hough transforms [18], 3-D moments [17] and surface features such as chord distributions [14] and radial axis distributions [15]. The work from Princeton has been particularly influential in this area and a recent paper on 3-D benchmarking compares a range of matching algorithms [16] in terms of retrieval performance using the Princeton Benchmark data set.…”

Section: Related Workmentioning

confidence: 99%

“…These include the D2 shape distribution descriptors from the Princeton Shape Retrieval and Analysis Group [14], the histogram descriptors from Paquet and Rioux [15] and the Area to Volume Ratio descriptor [23] which is a single statistic giving the ratio of the surface area of the model to its enclosed volume. The D2 descriptor records the distribution of distances between random points on the surface of the model and is rotation and translation invariant and robust to changes in mesh resolution.…”

Section: Multimodal Multimedia Retrievalmentioning

confidence: 99%

SCULPTEUR: Multimedia Retrieval for Museums

Goodall

Lewis

Martinez

et al. 2004

Lecture Notes in Computer Science

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Abstract. The paper describes the prototype design and development of a multimedia system for museums and galleries. Key elements in the system are the introduction of 3-D models of museum artefacts together with 3-D as well as 2-D content based retrieval and navigation facilities and the development of a semantic layer, centred on an ontology for museums, which aims to expose the richness of knowledge associated with the museum collections and facilitate concept based retrieval and navigation integrated with that based on content and metadata. Interoperability protocols are designed to allow external applications to access the collection and an example is given of an e-Learning facility which uses models extracted to a virtual museum.

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“…Based on the statistics, Osada et al proposed the descriptor named shape distribution (D2) [9]. Paquet et al proposed the method of cord histograms [10]. The ratio area/volume is used as a feature vector to describe the 3-D models by Zhang et al [17].…”

Section: Introductionmentioning

confidence: 99%

A 3-D Search engine based on Fourier series

Lmaati¹,

Oirrak²,

Aboutajdine³

et al. 2010

Computer Vision and Image Understanding

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The size of 3-D data stored around the Web has become bigger. Therefore the development of recognition applications and retrieval systems of 3-D models is important. In this paper we propose a new scheme to measure similarity between 3-D models. The main idea is to reconstruct a 3-D closed curve that represents a 3-D model given by a polygonal mesh, and to extract a signature from this 3-D closed curve using the Fourier series. The proposed descriptor needs CPCA (Continuous Principal Component Analysis) to align 3-D models into a canonical position. The feature vectors constructed using this method, named FSD (Fourier Series Descriptor) are invariants under rigid transformations composed of translation, rotation, flipping and scale; robust to noise and level of detail. A 3-D polygonal mesh model serves as a query for search by shape similarity in a large collection of 3-D models database using an interactive 3-D search engine.

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Nefertiti: a query by content software for three-dimensional models databases management

Cited by 55 publications

References 7 publications

Density-Based Shape Descriptors for 3D Object Retrieval

Density-Based Shape Descriptors for 3D Object Retrieval

SCULPTEUR: Multimedia Retrieval for Museums

A 3-D Search engine based on Fourier series

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