Daniel Scharstein - Publications

Journal articles

• Daniel Scharstein, Angela Dai, Daniel Kondermann, Torsten Sattler, and Konrad Schindler. Guest Editorial: Special Issue on Performance Evaluation in Computer Vision. International Journal of Computer Vision, 129:2029–2030, April 2021.
• Tianfan Xue, Andrew Owens, Daniel Scharstein, Michael Goesele, and Richard Szeliski. Multi-frame stereo matching with edges, planes, and superpixels. Image and Vision Computing, 9:103771, November 2019.
• Andreas Kuhn, Heiko Hirschmüller, Daniel Scharstein, and Helmut Mayer. A TV prior for high-quality scalable multi-view stereo reconstruction. International Journal of Computer Vision, 124(1):2–17, August 2017.
• Ayan Chakrabarti, Ying Xiong, Baochen Sun, Trevor Darrell, Daniel Scharstein, Todd Zickler, and Kate Saenko. Modeling radiometric uncertainty for vision with tone-mapped color images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(11):2185-2198, November 2014.
  
• Johannes Kopf, Fabian Langguth, Daniel Scharstein, Richard Szeliski, and Michael Goesele. Image-based rendering in the gradient domain. ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2013), 32(6), November 2013. Fraunhofer IGD Best Paper Award 2013 - Honorable Mention.
  
• Christopher Pal, Jerod Weinman, Lam Tran, and Daniel Scharstein. On learning conditional random fields for stereo. International Journal of Computer Vision, 99(3):319-337, September 2012.
  
• Sudipta Sinha, Johannes Kopf, Michael Goesele, Daniel Scharstein, and Richard Szeliski. Image-based rendering for scenes with reflections. ACM Transactions on Graphics (Proceedings of SIGGRAPH 2012), 31(4), July 2012.
  
• Simon Baker, Daniel Scharstein, J.P. Lewis, Stefan Roth, Michael Black, and Richard Szeliski. A Database and Evaluation Methodology for Optical Flow, International Journal of Computer Vision, 92(1):1-31, March 2011. See also the Middlebury Flow Page.
• Heiko Hirschmüller and Daniel Scharstein. Evaluation of stereo matching costs on images with radiometric differences. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(9):1582-1599, September 2009.
• Richard Szeliski, Ramin Zabih, Daniel Scharstein, Olga Veksler, Vladimir Kolmogorov, Aseem Agarwala, Marshall Tappen, and Carsten Rother. A comparative study of energy minimization methods for Markov random fields with smoothness-based priors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(6):1068-1080, June 2008. See also the Middlebury MRF Page.
• Amy Briggs, Carrick Detweiler, Yunpeng Li, Peter Mullen, and Daniel Scharstein. Matching scale-space features in 1D panoramas. Computer Vision and Image Understanding, 103(3):184-195, September 2006.
• Amy Briggs, Carrick Detweiler, Daniel Scharstein, and Alexander Vandenberg-Rodes. Expected shortest paths for landmark-based robot navigation. International Journal of Robotics Research (IJRR), 23(7-8):717-728, July-August 2004.
• Richard Szeliski and Daniel Scharstein. Sampling the disparity space image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(3):419-425, March 2004.
• Daniel Scharstein and Richard Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, 47(1/2/3):7-42, April-June 2002. See also the Middlebury Stereo Vision Page.
• Daniel Scharstein and Amy Briggs. Real-time recognition of self-similar landmarks. Image and Vision Computing, 19(11):763-772, September 2001.
• Daniel Scharstein and Richard Szeliski. Stereo matching with nonlinear diffusion. International Journal of Computer Vision, 28(2):155-174, June/July 1998.
• Matthew Dickerson and Daniel Scharstein. Optimal placement of convex polygons to maximize point containment. Computational Geometry: Theory and Applications, 11(1):1-16, August 1998.
• Alberto Segre and Daniel Scharstein. Bounded-overhead caching for definite-clause theorem proving. Journal of Automated Reasoning, 11:83-113, November 1993.

Refereed conference articles

• 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 Cohen, and Matt Uyttendaele. A practical stereo depth system for smart glasses. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2023), Vancouver, Canada, June 2023.
• Chun-Hao Huang, Hongwei Yi, Markus Höschle, Matvey Safroshkin, Tsvetelina Alexiadis, Senya Polikovsky, Daniel Scharstein, and Michael Black. Capturing and inferring dense full-body human-scene contact. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2022), New Orleans, LA, June 2022.
• Daniel Scharstein, Tatsunori Taniai, and Sudipta Sinha. Semi-global stereo matching with surface orientation priors. In International Conference on 3D Vision (3DV 2017), Qingdao, China, October 2017.
• Dylan Quenneville and Daniel Scharstein. Mondrian stereo. In IEEE International Conference on Image Processing (ICIP 2017), Beijing, China, September 2017.
• Andreas Kuhn, Helmut Mayer, Heiko Hirschmüller, and Daniel Scharstein. A TV prior for high-quality local multi-view stereo reconstruction. In International Conference on 3D Vision (3DV 2014), Tokyo, Japan, December 2014.
• Daniel Scharstein, Heiko Hirschmüller, York Kitajima, Greg Krathwohl, Nera Nesic, Xi Wang, and Porter Westling. High-resolution stereo datasets with subpixel-accurate ground truth. In German Conference on Pattern Recognition (GCPR 2014), Münster, Germany, September 2014. Best paper award. See also the 2014 Stereo Datasets Page.
• Sudipta Sinha, Daniel Scharstein, and Richard Szeliski. Efficient high-resolution stereo matching using local plane sweeps. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2014), Columbus, OH, June 2014. Supplementary materials.
• Michael Bleyer, Carsten Rother, Pushmeet Kohli, Daniel Scharstein, and Sudipta Sinha. Object stereo - joint stereo matching and object segmentation. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2011), pages 3081-3088, Colorado Springs, CO, June 2011.
• David Fouhey, Daniel Scharstein, and Amy Briggs. Multiple plane detection in image pairs using J-linkage. In 20th International Conference on Pattern Recognition (ICPR 2010), Istanbul, Turkey, August 2010.
• Ayan Chakrabarti, Daniel Scharstein, and Todd Zickler. An empirical camera model for Internet color vision. In British Machine Vision Conference (BMVC 2009), London, UK, September 2009. See also the Middlebury Color Page.
• Simon Baker, Daniel Scharstein, J.P. Lewis, Stefan Roth, Michael Black, and Richard Szeliski. A database and evaluation methodology for optical flow. In IEEE International Conference on Computer Vision (ICCV 2007), Rio de Janeiro, Brazil, October 2007. See also the Middlebury Flow Page.
• Daniel Scharstein and Christopher Pal. Learning conditional random fields for stereo. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007), Minneapolis, MN, June 2007.
• Heiko Hirschmüller and Daniel Scharstein. Evaluation of cost functions for stereo matching. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007), Minneapolis, MN, June 2007.
• Steven Seitz, Brian Curless, James Diebel, Daniel Scharstein, and Richard Szeliski. A comparison and evaluation of multi-view stereo reconstruction algorithms. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), volume 1, pages 519-526, New York, NY, June 2006. See also the Middlebury Multi View Page.
• Richard Szeliski, Ramin Zabih, Daniel Scharstein, Olga Veksler, Vladimir Kolmogorov, Aseem Agarwala, Marshall Tappen, and Carsten Rother. A comparative study of energy minimization methods for Markov random fields. In Ninth European Conference on Computer Vision (ECCV 2006), volume 2, pages 19-26, Graz, Austria, May 2006. See also the Middlebury MRF Page.
• Amy Briggs, Yunpeng Li, Daniel Scharstein, and Matt Wilder. Robot navigation using 1D panoramic images. In International Conference on Robotics and Automation (ICRA 2006), pages 2679-2685, Orlando, FL, May 2006.
• Amy Briggs, Yunpeng Li, and Daniel Scharstein. Feature matching across 1D panoramas. In Omnivis 2005, the sixth Workshop on Omnidirectional Vision (in conjunction with ICCV 2005), Beijing, China, October 2005.
• Amy Briggs, Carrick Detweiler, Peter Mullen, and Daniel Scharstein. Scale-space features in 1D omnidirectional images. In Omnivis 2004, the fifth Workshop on Omnidirectional Vision (in conjunction with ECCV 2004), pages 115-126, Prague, Czech Republic, May 2004.
• Daniel Scharstein and Richard Szeliski. High-accuracy stereo depth maps using structured light. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2003), volume 1, pages 195-202, Madison, WI, June 2003.
• Amy Briggs, Carrick Detweiler, Daniel Scharstein, and Alexander Vandenberg-Rodes. Expected shortest paths for landmark-based robot navigation. In Fifth International Workshop on Algorithmic Foundations of Robotics (WAFR 2002), Nice, France, December 2002.
• Richard Szeliski and Daniel Scharstein. Symmetric subpixel stereo matching. In Seventh European Conference on Computer Vision (ECCV 2002), volume 2, pages 525-540, Copenhagen, Denmark, May 2002.
• Daniel Scharstein, Richard Szeliski, and Ramin Zabih. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. In Workshop on Stereo and Multi-Baseline Vision (in conjunction with IEEE CVPR 2001), pages 131-140, Kauai, Hawaii, December 2001.
• Amy Briggs, Daniel Scharstein, and Stephen Abbott. Reliable mobile robot navigation from unreliable visual cues. In Fourth International Workshop on Algorithmic Foundations of Robotics (WAFR 2000), pages 349-362, Hanover, NH, March 2000.
• Amy Briggs, Daniel Scharstein, Darius Braziunas, Cristian Dima, and Peter Wall. Mobile robot navigation using self-similar landmarks. In International Conference on Robotics and Automation (ICRA 2000), pages 1428-1434, San Francisco, CA, April 2000.
• Daniel Scharstein and Amy Briggs. Fast recognition of self-similar landmarks. In Workshop on Perception for Mobile Agents (in conjunction with IEEE CVPR'99), pages 74-81, Fort Collins, CO, June 1999.
• Daniel Scharstein. Stereo vision for view synthesis. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'96), pages 852-858, San Francisco, CA, June 1996.
• Daniel Scharstein and Richard Szeliski. Stereo matching with non-linear diffusion. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'96), pages 343-350, San Francisco, CA, June 1996. Techreport version.
• Matthew Dickerson and Daniel Scharstein. Optimal placement of convex polygons to maximize point containment. In 7th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA'96), pages 114-121, Atlanta, GA, February 1996.
• Matthew Dickerson and Daniel Scharstein. The rotation diagram and optimal containing placements of a convex polygon. In 5th Video Review of Computational Geometry (with the 12th ACM Symposium on Computational Geometry), pages V9-V10, Philadelphia, PA, May 1996.
• Daniel Scharstein. Matching images by comparing their gradient fields. In 12th International Conference on Pattern Recognition (ICPR'94), volume 1, pages 572-575, Jerusalem, Israel, October 1994. Techreport version.

Books and book chapters

• Daniel Scharstein. View Synthesis Using Stereo Vision. Lecture Notes in Computer Science (LNCS), volume 1583. Springer Verlag, 1999. PDF.

• Richard Szeliski, Ramin Zabih, Daniel Scharstein, Olga Veksler, Vladimir Kolmogorov, Aseem Agarwala, Marshall Tappen, and Carsten Rother. A comparative study of energy minimization methods for MRFs. In Aandrew Blake, Pushmeet Kohli, and Carsten Rother, editors, Markov Random Fields for Vision and Image Processing, chapter 11, pages 167-182. MIT Press, 2011.
• Alberto Segre, Charles Elkan, Daniel Scharstein, Geoffrey Gordon, and Alexander Russell. Adaptive inference. In A. L. Meyrowitz and S. Chipman, editors, Foundations of Knowledge Acquisition: Machine Learning, volume 2 of The Springer International Series in Engineering and Computer Science, chapter 2, pages 43-81. Kluwer Academic, Norwell, MA, 1993.

PhD Thesis

• Daniel Scharstein. View Synthesis Using Stereo Vision. PhD thesis, Cornell University, January 1997.