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

Chaouch, Mohamed; Verroust-Blondet, Anne

doi:10.1109/icip.2007.4379599

Cited by 42 publications

(32 citation statements)

References 15 publications

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“…Chaouch and Verroust-Blondet [16] propose a method where a 3D model is projected to the faces of its bounding box, resulting in six depth buffers. Each depth buffer is then decomposed into a set of horizontal and vertical depth lines that are converted to state sequences, which describe the change in depth at neighboring pixels.…”

Section: Methods With a Fixed Number Of Viewsmentioning

confidence: 99%

A New Efficient Optimal 2D Views Selection Method Based on Pivot Selection Techniques for 3D Indexing and Retrieval

Satori¹,

Hanyf

2015

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Abstract:In this paper, we propose a new method for 2D/3D object indexing and retrieval. The principle consists of an automatic selection of optimal views by using an incremental algorithm based on pivot selection techniques for proximity searching in metric spaces. The selected views are afterward described by four well-established descriptors from the MPEG-7 standard, namely: the color structure descriptor (CSD), the scalable color descriptor (SCD), the edge histogram descriptor (EHD) and the color layout descriptor (CLD). We present our results on two databases: The Amsterdam Library of Images (ALOI-1000), consisting of 72,000 color images of views, and the Columbia Object Image Library (COIL-100), consisting of 7200 color images of views. The results prove the performance of the developed method and its superiority over the k-means algorithm and the automatic selection of optimal views proposed by Mokhtarian et al.

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Section: Methods With a Fixed Number Of Viewsmentioning

confidence: 99%

A New Efficient Optimal 2D Views Selection Method Based on Pivot Selection Techniques for 3D Indexing and Retrieval

Satori¹,

Hanyf

2015

Information

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“…Efforts along this line are mostly devoted to two stages: descriptive feature extraction from certain view images and appropriate comparison between sets of visual fea tures. For the former, typical approaches include Light Field descriptors [37], the Multi view Depth Line Approach (MDLA) [38], salient local visual features [39], Compact Multi View Descriptor (CMVD) [40], and View Context shape descriptor [41]. For the latter, basic work includes the Bag of Features based approach [42] and its variants such as Bag of Region Words [43] as well as more accurate 3D model alignment based methods [44].…”

Section: View Based Techniquesmentioning

confidence: 99%

“…To preserve the similarity between a query sketch image and a target 30 model in the ranking score, we add the following fitting constraint tenn: (38) where z, exp( d(s , m 1 ) 2 ; a2 ) is the similarity between the query sketch image and ith target 30 model. The optimal ran king score is obtained by minimizing following cost funct ion:…”

Section: Similarity Constrained Manifold Rankingmentioning

confidence: 99%

A comparison of 3D shape retrieval methods based on a large-scale benchmark supporting multimodal queries

et al. 2015

Computer Vision and Image Understanding

118

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a b s t r a c tLarge scale 3D shape retrieval has become an important research direction in content based 3D shape retrieval. To promote this research area, two Shape Retrieval Contest (SHREC) tracks on large scale com prehensive and sketch based 3D model retrieval have been organized by us in 2014. Both tracks were based on a unified large scale benchmark that supports multimodal queries (3D models and sketches). This benchmark contains 13680 sketches and 8987 3D models, divided into 171 distinct classes. It was compiled to be a superset of existing benchmarks and presents a new challenge to retrieval methods as it comprises generic models as well as domain specific model types. Twelve and six distinct 3D shape retrieval methods have competed with each other in these two contests, respectively. To measure and compare the performance of the participating and other promising Query by Model or Query by Sketch 3D shape retrieval methods and to solicit state of the art approaches, we perform a more comprehensive comparison of twenty six (eighteen originally participating algorithms and eight additional state of the art or new) retrieval methods by evaluating them on the common benchmark. The benchmark, results, and evaluation tools are publicly available at our websites

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“…Depth maps obtained from the rendering of the 3D object have been used in several retrieval approaches (e.g. [24]). Multiple descriptors can also be combined together, e.g., the work of Daras et Al.…”

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

Cited by 42 publications

References 15 publications

A New Efficient Optimal 2D Views Selection Method Based on Pivot Selection Techniques for 3D Indexing and Retrieval

A New Efficient Optimal 2D Views Selection Method Based on Pivot Selection Techniques for 3D Indexing and Retrieval

A comparison of 3D shape retrieval methods based on a large-scale benchmark supporting multimodal queries

Combining color and shape descriptors for 3D model retrieval

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