skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • Home
  • Search Results
  • Page 1 of 1

Search for: All records

Creators/Authors contains: "PNVR, Koutilya"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ; ; ; ; (, IEEE)
    Accepted Manuscript Full Text Available
  2. ; ; (, IEEE Computer Society Conference on Computer Vision and Pattern Recognition)
    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
    Accepted Manuscript Full Text Available
  3. ; ; (, IEEE Computer Society Conference on Computer Vision and Pattern Recognition)
    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
    Accepted Manuscript Full Text Available