CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Learning 3D Keypoint Descriptors for Non-Rigid Shape Matching
Hanyu Wang; Jianwei Guo; Dong-Ming Yan; Weize Quan; Xiaopeng Zhang
Conference Name15th European Conference on Computer Vision (ECCV)
Conference DateSeptember 8 – 14, 2018
Conference PlaceMunich, Germany
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.
Document Type会议论文
Corresponding AuthorDong-Ming Yan
AffiliationNLPR, Institute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Hanyu Wang,Jianwei Guo,Dong-Ming Yan,et al. Learning 3D Keypoint Descriptors for Non-Rigid Shape Matching[C],2018.
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