GIFT: A Real-Time and Scalable 3D Shape Search Engine

CASIA OpenIR > 模式识别实验室

	GIFT: A Real-Time and Scalable 3D Shape Search Engine
	Song Bai; Xiang Bai; Zhichao Zhou; Zhaoxiang Zhang; Longin Jan Latecki
	2016-06-27
会议名称	IEEE Conference on Computer Vision and Pattern Recognition
会议录名称	CVPR 2016
会议日期	27-30 June 2016
会议地点	Las Vegas, NV, USA
摘要	Projective analysis is an important solution for 3D shape retrieval, since human visual perceptions of 3D shapes rely on various 2D observations from different view points. Although multiple informative and discriminative views are utilized, most projection-based retrieval systems suffer from heavy computational cost, thus cannot satisfy the basic requirement of scalability for search engines. 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, referred as F-IF, is utilized to speed up the procedure of multi-view matching; (3) the second inverted file (S-IF), which captures a local distribution of 3D shapes in the feature manifold, is adopted for efficient context-based re-ranking. 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 the state-of-the-art methods significantly in retrieval accuracy on various shape benchmarks and competitions.
关键词	Pattern Recognition 3d Shape Retrieval
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/13249
专题	模式识别实验室
通讯作者	Zhaoxiang Zhang
推荐引用方式 GB/T 7714	Song Bai,Xiang Bai,Zhichao Zhou,et al. GIFT: A Real-Time and Scalable 3D Shape Search Engine[C],2016.