WarpNet: Weakly Supervised Matching for Single-View Reconstruction

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WarpNet: Weakly Supervised Matching for Single-View ReconstructionPublication Date: 6/1/2016

Event: CVPR 2016

Authors: Angjoo Kanazawa, University of Maryland, College Park; Manmohan Chandraker, David W. Jacobs, University of Maryland, College Park

Abstract: Our WarpNet matches images of objects in fine-grained datasets without using part annotations. It aligns an object in one image with a different object in another by exploiting a fine-grained dataset to create artificial data for training a Siamese network with an unsupervised discriminative learning approach. The output of the network acts as a spatial prior that allows generalization at test time to match real images across variations in appearance, viewpoint and articulation. This allows single-view reconstruction with quality comparable to using human annotation.

Publication Link: https://openaccess.thecvf.com/content_cvpr_2016/papers/Kanazawa_WarpNet_Weakly_Supervised_CVPR_2016_paper.pdf

Supplemental Publication Link: https://openaccess.thecvf.com/content_cvpr_2016/supplemental/Kanazawa_WarpNet_Weakly_Supervised_2016_CVPR_supplemental.zip