Abstract. 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.