SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation

• URL: http://arxiv.org/abs/2006.04026v1
• Date: Sun, 7 Jun 2020 02:45:33 GMT
• Title: SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation
• Authors: Koutilya PNVR, Hao Zhou, David Jacobs
• Abstract summary: We propose a novel method for combining synthetic and real images when training networks. We suggest a method for mapping both image types into a single, shared domain. Our experiments demonstrate significant improvements over the state-of-the-art in two important domains.
• Score: 18.29202999419042
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel method for combining synthetic and real images when training networks to determine geometric information from a single image. We suggest a method for mapping both image types into a single, shared domain. This is connected to a primary network for end-to-end training. Ideally, this results in images from two domains that present shared information to the primary network. Our experiments demonstrate significant improvements over the state-of-the-art in two important domains, surface normal estimation of human faces and monocular depth estimation for outdoor scenes, both in an unsupervised setting.

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