About Me

I’m Ke Wang (王珂), a PhD student in the 3D Computer Vision Group, Department of Computer Science, UNC Chapel Hill. I’m advised by Dr Jan-Michael Frahm and Dr. Enrique Dunn.

My research focuses on 3D computer vision, including depth map computations, 3D reconstruction from satellite images, large-scale video data association, image geo-localization, etc. Recently I’ve been using deep neural networks to perform parametric/semantic reconstructions from aerial/satellite imagery. I’m expected to finish my PhD in 2017.


Education


Publications

  1. Ke Wang, Enrique Dunn, Mikel Rodriguez, and Jan-Michael Frahm: Bringing 3d models together: Mining video liaisons in crowdsourced reconstructions. The 13th Asian Conference on Computer Vision (ACCV), 2016. [Paper] [Bibtex] [Poster] [Video]
  2. Shan Yang, Zherong Pan, Ke Wang, Tanya Amert, Licheng Yu, Tamara Berg, and Ming C. Lin. Physics-inspired garment recovery from a single-view image. ACM Transaction on Graphics (TOG), 2016. [Paper] [arXiv] [Video]
  3. Ke Wang, Craig Stutts, Enrique Dunn, and Jan-Michael Frahm: Efficient joint stereo estimation and land usage classification for multiview satellite data. IEEE Winter Conference on Applications of Computer Vision (WACV), 2016. [Paper] [Bibtex]
  4. Enliang Zheng, Ke Wang, Enrique Dunn, and Jan-Michael Frahm: Minimal solvers for 3d geometry from satellite imagery. IEEE International Conference on Computer Vision (ICCV), 2015. [Paper] [Bibtex]
  5. Enliang Zheng, Ke Wang, Enrique Dunn, and Jan-Michael Frahm: Joint object class sequencing and trajectory triangulation (jost). European Conference on Computer Vision (ECCV) (Oral), 2014. [Paper] [Bibtex]
  6. Yilin Wang, Ke Wang, Enrique Dunn, and Jan-Michael Frahm: Stereo under sequential optimal sampling: A statistical analysis framework for search space reduction. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014. [Paper] [Bibtex] [Video]
  7. Ke Wang, Enrique Dunn, Joseph Tighe, and Jan-Michael Frahm: Combining semantic scene priors and haze removal for single image depth estimation. IEEE Winter Conference on Applications of Computer Vision (WACV), 2014. [Paper] [Bibtex]

Industry Experience