3D Object Detection and Viewpoint Selection in Sketch Images Using Local Patch-Based Zernike Moments

Ta, Anh-Phuong; Wolf, Christian; Lavoué, Guillaume; Başkurt, Atilla

doi:10.1109/cbmi.2009.29

Cited by 6 publications

(7 citation statements)

References 17 publications

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“…(3) Compu ting fusion dissimilarity distance. First, we normal ize each row of the dissimilarity distance matrices resulting from different features, where cf (i,j ) and rf (i,j ) are the pre normalized and normalized dis tances between model i and model j respectively, while maX; and min; are the maximum and minimum distances in the ith row.…”

Section: E(r'qk)mentioning

confidence: 99%

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

et al. 2015

Computer Vision and Image Understanding

120

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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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Section: E(r'qk)mentioning

confidence: 99%

“…gap between the 2D queJY sketches and the 3D models, and because user sketches may vary widely in sketching style and level of detail, as well. It has many applications, including sketch based modeling and recognition, and sketch based 3D ani mation [3).…”

Section: Introductionmentioning

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

120

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“…This intuitive and conve nient scheme is easy for users to learn and use to search for 3D models. It is also popular and important for related applications such as sketch based modeling and recognition, as well as 3D animation production via 3D reconstruction of a scene of 2D storyboard [3].…”

Section: Introductionmentioning

confidence: 99%

A comparison of methods for sketch-based 3D shape retrieval

Godil

et al. 2014

Computer Vision and Image Understanding

112

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Sketch-based 3D shape retrieval has become an important research topic in content-based 3D object retrieval. To foster this research area, two Shape Retrieval Contest (SHREC) tracks on this topic have been organized by us in 2012 and 2013 based on a small-scale and large-scale benchmarks, respectively. Six and five (nine in total) distinct sketch-based 3D shape retrieval method have competed each other in these two contests, respectively. To measure and compare the performance of the top participating and other existing promising sketch-based 3D shape retrieval methods and solicit the state-of-the-art approaches, we perform a more comprehensive comparison of fifteen best (four top participating algorithms and eleven additional state-of-the-art methods) retrieval methods by completing the evaluation of each method on both benchmarks. The benchmarks, results, and evaluation tools for the two tracks are publicly available on our websites [1,2]

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“…Sketch-based 3D model retrieval in the context of a 2D sketch image of a scene, such as a 2D storyboard, is very important for the 3D scene reconstruction from a 2D sketch, which is usually a fundamental step for 3D animation production guided by 2D storyboards [174]. This is mainly because sketches are more human-friendly and people are more accustomed to "sketch" their ideas using a set of sketches.…”

Section: Scene Sketch-based 3d Model Retrievalmentioning

confidence: 99%

“…Proper 3D models are retrieved from available 3D databases to build a 3D scene [174] while keeping the context information in the original 2D scene consistent.…”

Section: Scene Sketch-based 3d Model Retrievalmentioning

confidence: 99%

View-based techniques for 3D model retrieval

Li¹

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With the increase in the number of available 3D models, the ability to accurately and efficiently search for 3D models is crucial in many applications such as Computer-Aided Design (CAD), on-line 3D model shopping and 3D game, movie and animation production. As a result, 3D model retrieval has become an important research area. In recent years, several typical algorithms that extract different types of 3D model features have been proposed. However, 3D model feature supporting multi-modal queries such as 3D models and 2D sketches is an important research direction which has little related work. In addition, 3D normalization is an important process in 3D model retrieval to extract rotation-dependent features and currently there still exists much room in terms of alignment accuracy and consistency. In this thesis work, we propose several algorithms to contribute solutions for the above issues. Motivated by the mechanism of human perception and multi-view vision, that is 3D shape information of a 3D object can be obtained based on multiple views, together with the retrieval performance comparison of previous retrieval work as well as the verifications of our proposed algorithms, we adopt a view-based approach which extracts features based on the rendered views of a 3D model. The first part of our work is dealing with 3D pose normalization. A novel Minimum Projection Area-based (MPA) alignment method is proposed for pose normalization based on the idea of successively finding two perpendicular principal axes with minimum projection area. Experimental results demonstrate that MPA has a good performance in finding accurate axes; can robustly find a consistent pose for similar models and outperforms PCA, CPCA, and NPCA in terms of 3D model retrieval performance. Next, we propose a view-based 3D model feature named view context to support both Query-by-Model and Query-by-Sketch retrieval. The view context of a particular view i This thesis could not be presented in this form without the help and support from so many people who are gratefully acknowledged here. Firstly, I would like to take this opportunity to appreciate Dr. Henry Johan, my supervisor, for his so much precious time and continuous effort dedicated for training me of doing good research. His invaluable suggestions, honest criticism and kind encouragement are constant sources of inspiration throughout the research. His personal enthusiasm, highly professional academic standards and serious attitude to research work will guide and benefit me in the future. I am also grateful to Prof. Seah Hock Soon for providing me a good research environment, and Dr. He Ying, Dr. Qian Kemao and Dr. Zheng Jianmin for their invaluable lectures and advices. Deep thanks also go to Mr. Budianto Tandianus and Mr. Nicholas Mario Wardhana for their help and the useful discussions during our weekly research meetings with our supervisor. Here, I also want to acknowledge Mr. Wei Yuanmin and Mr. Iskandarsyah in relation to the collaboration in 3D model alignment research.

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3D Object Detection and Viewpoint Selection in Sketch Images Using Local Patch-Based Zernike Moments

Cited by 6 publications

References 17 publications

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

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

A comparison of methods for sketch-based 3D shape retrieval

View-based techniques for 3D model retrieval

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