SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation

CVPR 2020  ·  Koutilya PNVR, Hao Zhou, David Jacobs ·

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. read more

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Monocular Depth Estimation KITTI Eigen split SharinGAN (proposed) absolute relative error 0.109 # 17
RMSE 3.77 # 3
RMSE log 0.19 # 1
Monocular Depth Estimation KITTI Eigen split SharinGAN Delta < 1.25 0.864 # 3
Delta < 1.25^2 0.954 # 3
Delta < 1.25^3 0.981 # 3
Monocular Depth Estimation Make3D SharinGAN Abs Rel 0.377 # 1
Sq Rel 4.9 # 1
RMSE 8.388 # 2


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