A 3D Model Retrieval System Using the Derivative Elevation and 3D-ART

Shih, Jau-Ling; Huang, Ting-Yen; Wang, Yu-Chen

doi:10.1109/apscc.2008.87

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…In this study, two descriptors, including the radial distance descriptor (RDD) [21] and the cylindrical projection descriptor (CPD) are used for 3D model retrieval. Before extracting the feature vectors, the 3D model is aligned according to the principal plane [12].…”

Section: The Proposed 3d Model Retrieval Methodsmentioning

confidence: 99%

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A 3D Model Retrieval System Based on the Cylindrical Projection Descriptor

Shih

Lee

Chou

et al. 2010

2010 International Conference on Broadband, Wireless Computing, Communication and Applications

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In recent years, the demand for a content-based 3D model retrieval system becomes an important issue. In this paper, the cylindrical projection descriptor (CPD) will be proposed for 3D model retrieval. To derive better retrieval results, the CPD will be combined with the radial distance descriptor (RDD). The experiments are conducted on the Princeton Shape Benchmark (PSB) database. Experiment results show that our proposed method is superior to others.

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Section: The Proposed 3d Model Retrieval Methodsmentioning

confidence: 99%

“…To derive better retrieval results, the CPD will be combined with the radial distance descriptor (RDD) [21]. The rest of the paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

A 3D Model Retrieval System Based on the Cylindrical Projection Descriptor

Shih

Lee

Chou

et al. 2010

2010 International Conference on Broadband, Wireless Computing, Communication and Applications

Self Cite

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“…resulting support regions, silhouette images…). The second one integrates some 3D information by considering the depth maps derived during the 3D/2D projection process 35,36,34 .…”

Section: Introductionmentioning

confidence: 99%

An overview of view-based 2D/3D indexing methods

2010

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This paper proposes a comprehensive overview of state of the art 2D/3D, view-based indexing methods. The principle of 2D/3D indexing methods consists of describing 3D models by means of a set of 2D shape descriptors, associated with a set of corresponding 2D views (under the assumption of a given projection model). Notably, such an approach makes it possible to identify 3D objects of interest from 2D images/videos. An experimental evaluation is also proposed, in order to examine the influence of the number of views and of the associated viewing angle selection strategies on the retrieval results. Experiments concern both 3D model retrieval and image recognition from a single view. Results obtained show promising performances, with recognition rates from a single view higher then 66%, which opens interesting perspectives in terms of semantic metadata extraction from still images/videos.

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