correspondence problem

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Deep Functional Maps: Structured Prediction for Dense Shape Correspondence
https://arxiv.org/abs/1704.08686

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Learning Dense Correspondence via 3D-guided Cycle Consistency https://people.eecs.berkeley.edu/~tinghuiz/papers/cvpr16_cycle.pdf

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Generic 3D Representation via Pose Estimation and Matching
http://cvgl.stanford.edu/papers/zamir_eccv16.pdf
ProjectPage: http://3drepresentation.stanford.edu/

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AnchorNet: A Weakly Supervised Network to Learn Geometry-sensitive Features For Semantic Matching https://arxiv.org/abs/1704.04749

Xiaowei: test

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Self-Supervised Visual Descriptor Learning for Dense Correspondence https://homes.cs.washington.edu/~tws10/3163.pdf

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Universal Correspondence Network http://cvgl.stanford.edu/projects/ucn/choy_nips16.pdf
ProjectPage: http://cvgl.stanford.edu/projects/ucn/