3D Model Retrieval Based on Adaptive Views Clustering

Ansary, Tarik Filali; Daoudi, Mohamed; Vandeborre, Jean-Phillipe

doi:10.1007/11552499_53

Cited by 15 publications

(9 citation statements)

References 10 publications

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“…The problem of this approach is in the choice of: Normalization of the pose, the projection area, the size of image, the nature of the 2D shape descriptors and the size of the signature. The authors (Ricard et al, 2005;Ansary et al, 2005;Mahmoudi and Daoudi, 2002;Nidhal and Lamis, 2014) have proposed methods of optimal selection of 2D views to represent a 3D model. Each 3D object is represented by a view set called the characteristic views.…”

Section: Approach Based Viewsmentioning

confidence: 99%

A New Method for 3d Shape Indexing and Retrieval in Large Database by Using the Level Cut

Elkhal¹,

Lakehal²,

Satori³

2014

Journal of Computer Science

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In this study, we propose a new method for indexing and retrieval of 3D models in large databases based on binary images extracted from the 3D object called "level cut" LC. These cuts are obtained by the intersection of the set of the plans with the 3D object. A set of equidistant parallel plans generates by the intersection with the 3D object a set of cuts that used to indexing the 3D model. We are based on these cuts to describe the 3D object by using the vectors descriptors based on these cuts. To validate our descriptor we extract a test database from the NTU base. The robustness of our descriptor is well demonstrated by the comparison with the two will known descriptors, the 3D Zernike descriptor and the 3D invariant moment's descriptor. The topological problem of the external surfaces that representing the 3D object has been confronted, that shows the superiority of the cut method, because it is keeping the external surfaces details of the 3D object during the cutting step.Keywords: Characteristics Level Cut, Vector Descriptor, 3D Shape Indexing and Retrieval, X-means Algorithm, Similarity Measuring INTROCUCTIONRecently, the technological development of 3D modeling tools is forever exploited. Many 3D models are now downloadable for free thanks to the internet, as well as the large databases of 3D models have become available in the market. The retrieval of these models in databases demands sophisticated methods for a rapid and effective response.Among the indexation approaches of 3D models, we find the 2D/3D approach (Haris et al., 2014;Jain and Singh, 2013;Li and Johan, 2013;Petre et al., 2010) which is based on a set of images (the characteristic views, images of depths, the slices...). The importance of this approach is extract for each image a descriptor vector for construct a set of vectors descriptors associated with the object to index. The similarity measure between two 3D objects then returns to calculate the distance between the two sets of descriptors representing them. The extraction of information of the 3D object in a robust and efficient way Recently, the technological development of 3D has become a real challenge. The proposed method permits to deal with this problem. Given a 3D object, the method starts by a normalization step of the processed object using the CPCA technique. Then, we extract a set of cuts based on our cutting method. This set of cuts (binary images) will be indexed by a 2D image descriptor to construct the descriptor vector associated to the 3D model, using the Hausdorff distance to calculate the distance between the vectors descriptors which allows us to measure the similarity between the 3D objects.The proposed descriptor shows the robustness in terms of response to the query provided by the user and the comparative study between the proposed descriptor and two other descriptors well known of the 3D/3D approach, the 3D Zernike moments and 3D invariant moments shows considerable results.

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Section: Approach Based Viewsmentioning

confidence: 99%

A New Method for 3d Shape Indexing and Retrieval in Large Database by Using the Level Cut

Elkhal¹,

Lakehal²,

Satori³

2014

Journal of Computer Science

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“…But the large number of viewpoints also give rise to the problems as follows: 1) a lot of information redundancy occurs in the collection of the 2D views due to the overlap between the view coverage of adjacent viewpoints; 2) the pair-wise comparison of the 2D views taken from the viewpoints is quite time consuming. Several approaches have been proposed to address these issues, including the adaptiveview-clustering method reported by Ansary et al [13], the view-context method reported by Li et al [14] and the probabilistic-graphical-matching method in [12].…”

Section: Introductionmentioning

confidence: 99%

Retrieving 3D Model Using Compound-Eye Visual Representation

Zhang

Bai

et al. 2013

2013 International Conference on Computer-Aided Design and Computer Graphics

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This paper describes a novel method for retrieving 3D models. Following the principle of compound-eye vision, the proposed method represents a 3D model as a spherical image, and discriminates different 3D models using their corresponding spherical images. Meanwhile, by borrowing the concept of the Scale-Invariant Feature Transform (SIFT) algorithm, we design a feature extraction algorithm, named Spherical-SIFT, for extracting the salient local features on spherical images. Moreover, the Bag-of-Features approach is employed so as to achieve efficient comparison of different 3D models. The experimental results show the superior performance of our method over pervious methods.

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“…The dissimilarity between two 3D models is defined as the minimal dissimilarity of the silhouettes over all rotations and all pairs of vertices on the corresponding dodecahedrons. Filali Ansary et al [7] introduce a novel probabilistic Bayesian using a characteristic views selection. Curvature scale space (CSS) descriptor is used in [8].…”

Section: Introductionmentioning

confidence: 99%

A New Descriptor for 2D Depth Image Indexing and 3D Model Retrieval

Chaouch

Verroust-Blondet

2007

2007 IEEE International Conference on Image Processing

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We present here a new descriptor for depth images adapted to 2D/3D model matching and retrieving. We propose a representation of a 3D model by 20 depth images rendered from the vertices of a regular dodecahedron. One depth image of a 3D model is associated to a set of depth lines which will be afterward transformed into sequences. The depth sequence information provides a more accurate description of 3D shape boundaries than using other 2D shape descriptors. Similarity computing is performed when dynamic programming distance (DPD) is used to compare the depth line descriptors. The DPD leads to an accurate matching of sequences even in the presence of local shifting on the shape. Results on a large 3D database show efficiency of our 2D/3D approach.Index Terms-Depth image indexing, depth sequence, 2D/3D description, Content-based 3D model retrieval

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3D Model Retrieval Based on Adaptive Views Clustering

Cited by 15 publications

References 10 publications

A New Method for 3d Shape Indexing and Retrieval in Large Database by Using the Level Cut

A New Method for 3d Shape Indexing and Retrieval in Large Database by Using the Level Cut

Retrieving 3D Model Using Compound-Eye Visual Representation

A New Descriptor for 2D Depth Image Indexing and 3D Model Retrieval

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