Daniel Scharstein - Publications

Journal articles

• D. Scharstein, A. Dai, D. Kondermann, T. Sattler, and K. Schindler. Guest Editorial: Special Issue on Performance Evaluation in Computer Vision. International Journal of Computer Vision, 129:2029–2030, April 2021.
• T. Xue, A. Owens, D. Scharstein, M. Goesele, and R. Szeliski. Multi-frame stereo matching with edges, planes, and superpixels. Image and Vision Computing, 9:103771, November 2019.
• A. Kuhn, H. Hirschmüller, D. Scharstein, and H. Mayer. A TV prior for high-quality scalable multi-view stereo reconstruction. International Journal of Computer Vision, 124(1):2–17, August 2017.
• A. Chakrabarti, Y. Xiong, B. Sun, T. Darrell, D. Scharstein, T. Zickler, and K. 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.
  
• J. Kopf, F. Langguth, D. Scharstein, R. Szeliski, and M. 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.
  
• C. Pal, J. Weinman, L. Tran, and D. Scharstein. On learning conditional random fields for stereo. International Journal of Computer Vision, 99(3):319-337, September 2012.
  
• S. Sinha, J. Kopf, M. Goesele, D. Scharstein, and R. Szeliski. Image-based rendering for scenes with reflections. ACM Transactions on Graphics (Proceedings of SIGGRAPH 2012), 31(4), July 2012.
  
• S. Baker, D. Scharstein, J.P. Lewis, S. Roth, M. Black, and R. 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.
• H. Hirschmüller and D. 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.
• R. Szeliski, R. Zabih, D. Scharstein, O. Veksler, V. Kolmogorov, A. Agarwala, M. Tappen, and C. 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.
• A. Briggs, C. Detweiler, Y. Li, P. Mullen, and D. Scharstein. Matching scale-space features in 1D panoramas. Computer Vision and Image Understanding, 103(3):184-195, September 2006.
• A. Briggs, C. Detweiler, D. Scharstein, and A. Vandenberg-Rodes. Expected shortest paths for landmark-based robot navigation. International Journal of Robotics Research (IJRR), 23(7-8):717-728, July-August 2004.
• R. Szeliski and D. Scharstein. Sampling the disparity space image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(3):419-425, March 2004.
• D. Scharstein and R. 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.
• D. Scharstein and A. Briggs. Real-time recognition of self-similar landmarks. Image and Vision Computing, 19(11):763-772, September 2001.
• D. Scharstein and R. Szeliski. Stereo matching with nonlinear diffusion. International Journal of Computer Vision, 28(2):155-174, June/July 1998.
• M. Dickerson and D. Scharstein. Optimal placement of convex polygons to maximize point containment. Computational Geometry: Theory and Applications, 11(1):1-16, August 1998.
• A. M. Segre and D. Scharstein. Bounded-overhead caching for definite-clause theorem proving. Journal of Automated Reasoning, 11:83-113, November 1993.

Refereed conference articles

• J. Wang, D. Scharstein, A. Bapat, K. Blackburn-Matzen, M. Yu, J. Lehman, S. Alsisan, Y. Wang, S. Tsai, J.-M. Frahm, Z. He, P. Vajda, M. Cohen, and M. 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.
• C.-H. Huang, H. Yi, M. Hoeschle, M. Safroshkin, T. Alexiadis, S. Polikovsky, D. Scharstein, and M. 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.
• D. Scharstein, T. Taniai, and S. Sinha. Semi-global stereo matching with surface orientation priors. In International Conference on 3D Vision (3DV 2017), Qingdao, China, October 2017.
• D. Quenneville and D. Scharstein. Mondrian stereo. In IEEE International Conference on Image Processing (ICIP 2017), Beijing, China, September 2017.
• A. Kuhn, H. Mayer, H. Hirschmüller, and D. Scharstein. A TV prior for high-quality local multi-view stereo reconstruction. In International Conference on 3D Vision (3DV 2014), Tokyo, Japan, December 2014.
• D. Scharstein, H. Hirschmüller, Y. Kitajima, G. Krathwohl, N. Nesic, X. Wang, and P. 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.
• S. Sinha, D. Scharstein, and R. 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.
• M. Bleyer, C. Rother, P. Kohli, D. Scharstein, and S. 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.
• D. Fouhey, D. Scharstein, and A. Briggs. Multiple plane detection in image pairs using J-linkage. In 20th International Conference on Pattern Recognition (ICPR 2010), Istanbul, Turkey, August 2010.
• A. Chakrabarti, D. Scharstein, and T. 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.
• S. Baker, D. Scharstein, J.P. Lewis, S. Roth, M. Black, and R. 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.
• D. Scharstein and C. Pal. Learning conditional random fields for stereo. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007), Minneapolis, MN, June 2007.
• H. Hirschmüller and D. 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.
• S. Seitz, B. Curless, J. Diebel, D. Scharstein, and R. 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.
• R. Szeliski, R. Zabih, D. Scharstein, O. Veksler, V. Kolmogorov, A. Agarwala, M. Tappen, and C. 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.
• A. Briggs, Y. Li, D. Scharstein, and M. Wilder. Robot navigation using 1D panoramic images. In International Conference on Robotics and Automation (ICRA 2006), pages 2679-2685, Orlando, FL, May 2006.
• A. Briggs, Y. Li, and D. Scharstein. Feature matching across 1D panoramas. In Omnivis 2005, the sixth Workshop on Omnidirectional Vision (in conjunction with ICCV 2005), Beijing, China, October 2005.
• A. Briggs, C. Detweiler, P. Mullen, and D. 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.
• D. Scharstein and R. 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.
• A. Briggs, C. Detweiler, D. Scharstein, and A. 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.
• R. Szeliski and D. Scharstein. Symmetric subpixel stereo matching. In Seventh European Conference on Computer Vision (ECCV 2002), volume 2, pages 525-540, Copenhagen, Denmark, May 2002.
• D. Scharstein, R. Szeliski, and R. 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.
• A. Briggs, D. Scharstein, and S. 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.
• A. Briggs, D. Scharstein, D. Braziunas, C. Dima, and P. 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.
• D. Scharstein and A. 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.
• D. 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.
• D. Scharstein and R. 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.
• M. Dickerson and D. 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.
• M. Dickerson and D. 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.
• D. 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

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

• R. Szeliski, R. Zabih, D. Scharstein, O. Veksler, V. Kolmogorov, A. Agarwala, M. Tappen, and C. Rother. A comparative study of energy minimization methods for MRFs. In A. Blake, P. Kohli, and C. Rother, editors, Markov Random Fields for Vision and Image Processing, chapter 11, pages 167-182. MIT Press, 2011.
• A. M. Segre, C. Elkan, D. Scharstein, G. Gordon, and A. 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

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