We propose a novel method for combining synthetic and
real images when training networks to determine geomet-
ric 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 train-
ing. 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.
more »
« less
SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation
We propose a novel method for combining synthetic and
real images when training networks to determine geomet-
ric 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 train-
ing. 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.
more »
« less
- Award ID(s):
- 1910132
- NSF-PAR ID:
- 10178925
- Date Published:
- Journal Name:
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition
- ISSN:
- 2332-564X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
