no code implementations • CVPR 2023 • Jialiang Wang, Daniel Scharstein, Akash Bapat, Kevin Blackburn-Matzen, Matthew Yu, Jonathan Lehman, Suhib Alsisan, Yanghan Wang, Sam Tsai, Jan-Michael Frahm, Zijian He, Peter Vajda, Michael F. Cohen, Matt Uyttendaele
We present the design of a productionized end-to-end stereo depth sensing system that does pre-processing, online stereo rectification, and stereo depth estimation with a fallback to monocular depth estimation when rectification is unreliable.
2 code implementations • CVPR 2022 • Chun-Hao P. Huang, Hongwei Yi, Markus Höschle, Matvey Safroshkin, Tsvetelina Alexiadis, Senya Polikovsky, Daniel Scharstein, Michael J. Black
We capture a new dataset called RICH for "Real scenes, Interaction, Contact and Humans."
Ranked #3 on Contact Detection on BEHAVE
no code implementations • 3 Dec 2017 • Daniel Scharstein, Tatsunori Taniai, Sudipta N. Sinha
In this paper we evaluate plane orientation priors derived from stereo matching at a coarser resolution and show that such priors can yield significant performance gains for difficult weakly-textured scenes.
no code implementations • CVPR 2014 • Sudipta N. Sinha, Daniel Scharstein, Richard Szeliski
We present a stereo algorithm designed for speed and efficiency that uses local slanted plane sweeps to propose disparity hypotheses for a semi-global matching algorithm.
no code implementations • 27 Nov 2013 • Ayan Chakrabarti, Ying Xiong, Baochen Sun, Trevor Darrell, Daniel Scharstein, Todd Zickler, Kate Saenko
To produce images that are suitable for display, tone-mapping is widely used in digital cameras to map linear color measurements into narrow gamuts with limited dynamic range.
1 code implementation • International Journal of Computer Vision 2010 • Simon Baker, Daniel Scharstein, J. P. Lewis, Stefan Roth, Michael J. Black, Richard Szeliski
The quantitative evaluation of optical flow algorithms by Barron et al. (1994) led to significant advances in performance.