3D model comparison using spatial structure circular descriptor

Gao, Yue; Dai, Qionghai; Zhang, Naiyao

doi:10.1016/j.patcog.2009.07.012

Cited by 128 publications

(44 citation statements)

References 28 publications

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“…According to [22], 3D shape descriptors can be divided into four categories, i.e., histogram-based method [9], [23]- [25], transformbased method [26]- [29], graph-based method [30]- [32] and view-based method [21], [33], [34].…”

Section: Related Workmentioning

confidence: 99%

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Learning View-Model Joint Relevance for 3D Object Retrieval

Lü

Xue

et al. 2015

IEEE Trans. on Image Process.

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Abstract-3D object retrieval has attracted extensive research efforts and become an important task in recent years. It is noted that how to measure the relevance between 3D objects is still a difficult issue. Most of the existing methods employ just the model-based or view-based approaches, which may lead to incomplete information for 3D object representation. In this paper, we propose to jointly learn the view-model relevance among 3D objects for retrieval, in which the 3D objects are formulated in different graph structures. With the view information, the multiple views of 3D objects are employed to formulate the 3D object relationship in an object hypergraph structure. With the model data, the model-based features are extracted to construct an object graph to describe the relationship among the 3D objects. The learning on the two graphs is conducted to estimate the relevance among the 3D objects, in which the view/model graph weights can be also optimized in the learning process. This is the first work to jointly explore the view-based and model-based relevance among the 3D objects in a graphbased framework. The proposed method has been evaluated in three data sets. The experimental results and comparison with the state-of-the-art methods demonstrate the effectiveness on retrieval accuracy of the proposed 3D object retrieval method.Index Terms-3D object retrieval, view information, model data, joint learning.

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Section: Related Workmentioning

confidence: 99%

“…Following the method in [20], an object hypergraph is constructed using the view star expansion. In the model part, the spatial structure circular descriptor [21] is extracted and a simple graph is generated using the pairwise object distances. In this way, the view information and the model data can be formulated in two graph structures.…”

mentioning

confidence: 99%

Learning View-Model Joint Relevance for 3D Object Retrieval

Lü

Xue

et al. 2015

IEEE Trans. on Image Process.

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“…Recently, Gao et al 13 proposed another view-based descriptor called the SSCD, which can preserve the global spatial structure of stereo models and is invariant to rotation and scaling. In addition, all spatial information of stereo models can be represented by the SSCD without redundancy.…”

Section: Related Workmentioning

confidence: 99%

“…Stereo model comparison is then based on the comparison of these six elevations. Recently, Gao et al 13 proposed a spatial structure circular descriptor (SSCD) method to compare two stereo models. In this method, the stereo model is projected onto a plane, and the comparison between two stereo models is based on matching between the two groups of SSCD images.…”

Section: Introductionmentioning

confidence: 99%

A novel stereo-model retrieval method in manufacturing

Chen

Zhang

Lin

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part B:

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With the development of computer vision technologies, manufacturing is now using stereo models for production, and the amount of stereo models generated recently has increased significantly. Thus, a fast and reliable model retrieval algorithm has become important. In this article, we propose a novel view-based stereo-model retrieval algorithm using a modified principal component analysis algorithm. Unlike a traditional principal component analysis that uses the origin of a two-dimensional image, we apply principal component analysis to extract eigenvectors for each stereo model. First, we extract a set of two-dimensional images from different directions of the stereo object. Because each two-dimensional image can be seen as one sample of the stereo object, we utilize principal component analysis to extract the eigenvectors as a dictionary for each stereo object. Then, those eigenvectors are used to rebuild the query. Finally, the reconstruction residual is applied to represent the similarity between the query and the candidate stereo object. Experimentally, the proposed retrieval algorithm has been evaluated using the ETH-80 and ALOI datasets. Experimental results and comparisons with other methods show the effectiveness of the proposed approach that can be used in engineering manufacturing and computer-aided design applications.

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“…This method was extended in [13] by computing the distances on a regular voxel grid. Gao et al [14] introduced the spatial structure circular descriptor, that allows to capture the spatial structure of the 3D model into a set of images that can then be compared for retrieval purposes. Another global method, the Relativistic Impact Descriptor [15] associates to each 3D object a curved space modified by its mass from which two invariant descriptors are extracted.…”

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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3D model comparison using spatial structure circular descriptor

Cited by 128 publications

References 28 publications

Learning View-Model Joint Relevance for 3D Object Retrieval

Learning View-Model Joint Relevance for 3D Object Retrieval

A novel stereo-model retrieval method in manufacturing

Combining color and shape descriptors for 3D model retrieval

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