A Compact Multi-view Descriptor for 3D Object Retrieval

Daras, Petros; Αξενόπουλος, Απόστολος

doi:10.1109/cbmi.2009.15

Cited by 48 publications

(33 citation statements)

References 15 publications

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“…Several researches have been conducted to group 3D models into corresponding categories by matching the features and comparing the similarities of the models [5][6][7][8][9][10][11]14,[19][20][21][22]. The matching and comparison are implemented on a huge dataset containing various models of different poses and shapes.…”

Section: Related Workmentioning

confidence: 99%

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3D Model Identification Using Weighted Implicit Shape Representation and Panoramic View

Jin

Kim

2017

Applied Sciences

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Abstract:In this paper, we propose a 3 dimensional (3D) model identification method based on weighted implicit shape representation (WISR) and panoramic view. The WISR is used for 3D shape normalization. The 3D shape normalization method normalizes a 3D model by scaling, translation, and rotation with respect to the scale factor, center, and principal axes. The major advantage of the WISR is reduction of the influences caused by shape deformation and partial removal. The well-known scale-invariant feature transform descriptors are extracted from the panoramic view of the 3D model for feature matching. The panoramic view is a range image obtained by projecting a 3D model to the surface of a cylinder which is parallel to a principal axis determined by the 3D shape normalization. Because of using only one range image, the proposed method can provide small size of features and fast matching speed. The precision of the identification is 92% with 1200 models that consist of 24 deformed versions of 50 classes. The average feature size and matching time are 4.1 KB and 1.9 s.

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Section: Related Workmentioning

confidence: 99%

“…However, the geometry and topology based methods are generally computationally cost and are fragile to 3D model removal. View-based methods have a high discriminative property for 3D model representation [5][6][7][8][9][10][11]. A 2D view image is a range image obtained from a viewpoint located on a 3D model's bounding sphere.…”

Section: Related Workmentioning

confidence: 99%

3D Model Identification Using Weighted Implicit Shape Representation and Panoramic View

Jin

Kim

2017

Applied Sciences

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“…Recently, a Compact Multi-View Descriptor (CMVD) was proposed by 3 . The authors tested the CMVD on both binary and depth images.…”

Section: Methods Using Evenly Distributed Viewing Anglesmentioning

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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“…In [20] 5 different groups of views are extracted from the model and then a probabilistic approach is used to find the models that maximize the a posterior probability given the query model. The method of [21] instead extracts 2D rotation-invariant shape descriptors from a set of views and combines this information into a global shape similarity measure. Chen et al [22] compute the light fields of the 3D objects and extract the descriptors from their silhouettes.…”

Section: Related Workmentioning

confidence: 99%

Combining color and shape descriptors for 3D model retrieval

Pasqualotto

Zanuttigh

Cortelazzo

2013

Signal Processing: Image Communication

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Nowadays many three dimensional models feature color information together with the shape description. However current content-based retrieval schemes for 3D models are based on shape information only and ignore color clues. The significance of shape versus color clues for 3D model retrieval is instead a fundamental issue still almost unexplored at this time. A possible approach is to extend shape-based 3D model retrieval methods of proven effectiveness in order to include color. This work follows such rationale and introduces an extended version of the spin-image descriptor that can account also for color data. The comparison of color descriptors is performed using a novel scheme that allows to recognize as similar also objects with different colors but distributed in the same way over the shape. Shape and color similarity are finally combined together by an algorithm based on fuzzy logic. Experimental results show how the joint use of color and shape data allows to obtain better results than each of the two types of information alone. Comparisons with state-of-the-art content-based retrieval methods for 3D models also show how the proposed scheme outperforms standard solutions on object classes with meaningful color information.

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A Compact Multi-view Descriptor for 3D Object Retrieval

Cited by 48 publications

References 15 publications

3D Model Identification Using Weighted Implicit Shape Representation and Panoramic View

3D Model Identification Using Weighted Implicit Shape Representation and Panoramic View

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

Combining color and shape descriptors for 3D model retrieval

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