GIFT: Towards Scalable 3D Shape Retrieval
文献类型：期刊论文
| 作者 | Bai, Song1; Bai, Xiang1; Zhou, Zhichao1; Zhang, Zhaoxiang2 ; Tian, Qi3; Latecki, Longin Jan4
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| 刊名 | IEEE TRANSACTIONS ON MULTIMEDIA
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| 出版日期 | 2017-06-01 |
| 卷号 | 19期号:6页码:1257-1271 |
| 关键词 | 3d Shape Retrieval Cnn Shape Retrieval Contest (Shrec) |
| DOI | 10.1109/TMM.2017.2652071 |
| 文献子类 | Article |
| 英文摘要 | Projective analysis is an important solution in three-dimensional (3D) shape retrieval, since human visual perceptions of 3D shapes rely on various 2D observations from different viewpoints. Although multiple informative and discriminative views are utilized, most projection-based retrieval systems suffer from heavy computational cost, and thus cannot satisfy the basic requirement of scalability for search engines. In the past three years, shape retrieval contest (SHREC) pays much attention to the scalability of 3D shape retrieval algorithms, and organizes several large scale tracks accordingly [1]-[3]. However, the experimental results indicate that conventional algorithms cannot be directly applied to large datasets. In this paper, we present a real-time 3D shape search engine based on the projective images of 3D shapes. The real-time property of our search engine results from the following aspects: 1) efficient projection and view feature extraction using GPU acceleration; 2) the first inverted file, called F-IF, is utilized to speed up the procedure of multiview matching; and 3) the second inverted file, which captures a local distribution of 3D shapes in the feature manifold, is adopted for efficient context-based reranking. As a result, for each query the retrieval task can be finished within one second despite the necessary cost of IO overhead. We name the proposed 3D shape search engine, which combines GPU acceleration and inverted file (twice), as GIFT. Besides its high efficiency, GIFT also outperforms state-of-the-art methods significantly in retrieval accuracy on various shape benchmarks (ModelNet40 dataset, ModelNet10 dataset, PSB dataset, McGill dataset) and competitions (SHREC14LSGTB, ShapeNet Core55, WM-SHREC07). |
| WOS关键词 | BAG-OF-FEATURES ; MODEL RETRIEVAL ; DESCRIPTORS ; RECOGNITION ; SEARCH ; CLASSIFICATION ; REPRESENTATION ; COVARIANCE ; DIFFUSION ; DISTANCE |
| WOS研究方向 | Computer Science ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000404059400012 |
| 资助机构 | National Natural Science Foundation of China(61231010 ; China Scholarship Council ; National Science Foundation(IIS-1302164) ; ARO(W911NF-15-1-0290) ; Faculty Research Gift Awards by NEC Laboratories of America and Blippar ; 61573160 ; 61429201) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/15233]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 作者单位 | 1.Huazhong Univ Sci & Technol, Sch Elect Informat & Commun, Wuhan 430074, Peoples R China 2.Chinese Acad Sci, Inst Automat, Ctr Brain Inspired Intelligence, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China 3.Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX 78249 USA 4.Temple Univ, Dept Comp & Informat Sci, Philadelphia, PA 19122 USA |
| 推荐引用方式 GB/T 7714 | Bai, Song,Bai, Xiang,Zhou, Zhichao,et al. GIFT: Towards Scalable 3D Shape Retrieval[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2017,19(6):1257-1271. |
| APA | Bai, Song,Bai, Xiang,Zhou, Zhichao,Zhang, Zhaoxiang,Tian, Qi,&Latecki, Longin Jan.(2017).GIFT: Towards Scalable 3D Shape Retrieval.IEEE TRANSACTIONS ON MULTIMEDIA,19(6),1257-1271. |
| MLA | Bai, Song,et al."GIFT: Towards Scalable 3D Shape Retrieval".IEEE TRANSACTIONS ON MULTIMEDIA 19.6(2017):1257-1271. |
入库方式： OAI收割
来源：自动化研究所
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