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	Learning 3D Keypoint Descriptors for Non-Rigid Shape Matching
	Hanyu Wang; Jianwei Guo; Dong-Ming Yan; Weize Quan; Xiaopeng Zhang
	2018-09
会议日期	September 8 – 14, 2018
会议地点	Munich, Germany
英文摘要	In this paper, we present a novel deep learning framework that derives discriminative local descriptors for 3D surface shapes. In contrast to previous convolutional neural networks (CNNs) that rely on rendering multi-view images or extracting intrinsic shape properties, we parameterize the multi-scale localized neighborhoods of a keypoint into regular 2D grids, which are termed as ‘geometry images’. The benefits of such geometry images include retaining sufficient geometric information, as well as allowing the usage of standard CNNs. Specifically, we leverage a triplet network to perform deep metric learning, which takes a set of triplets as input, and a newly designed triplet loss function is minimized to distinguish between similar and dissimilar pairs of keypoints. At the testing stage, given a geometry image of a point of interest, our network outputs a discriminative local descriptor for it. Experimental results for non-rigid shape matching on several benchmarks demonstrate the superior performance of our learned descriptors over traditional descriptors and the state-of-the-art learning-based alternatives.
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/21688]
专题	自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
通讯作者	Dong-Ming Yan
作者单位	NLPR, Institute of Automation, Chinese Academy of Sciences
推荐引用方式 GB/T 7714	Hanyu Wang,Jianwei Guo,Dong-Ming Yan,et al. Learning 3D Keypoint Descriptors for Non-Rigid Shape Matching[C]. 见:. Munich, Germany. September 8 – 14, 2018.

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