Daniel Scharstein

21.7k total citations · 10 hit papers
41 papers, 13.4k citations indexed

About

Daniel Scharstein is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Media Technology. According to data from OpenAlex, Daniel Scharstein has authored 41 papers receiving a total of 13.4k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Computer Vision and Pattern Recognition, 10 papers in Aerospace Engineering and 6 papers in Media Technology. Recurrent topics in Daniel Scharstein's work include Advanced Vision and Imaging (32 papers), Advanced Image Processing Techniques (15 papers) and Advanced Image and Video Retrieval Techniques (15 papers). Daniel Scharstein is often cited by papers focused on Advanced Vision and Imaging (32 papers), Advanced Image Processing Techniques (15 papers) and Advanced Image and Video Retrieval Techniques (15 papers). Daniel Scharstein collaborates with scholars based in United States, Germany and United Kingdom. Daniel Scharstein's co-authors include Rick Szeliski, Richard Szeliski, Heiko Hirschmüller, James Diebel, Brian Curless, Steven M. Seitz, Ramin Zabih, Chris Pal, Michael J. Black and John Lewis and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, ACM Transactions on Graphics and International Journal of Computer Vision.

In The Last Decade

Daniel Scharstein

41 papers receiving 12.7k citations

Hit Papers

A Taxonomy and Evaluation... 2002 2026 2010 2018 2002 2006 2010 2003 2007 1000 2.0k 3.0k 4.0k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
    2002 4.5k citations Daniel Scharstein, Rick Szeliski profile →
  2. A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
    2006 1.6k citations Daniel Scharstein, Richard Szeliski et al. profile →
  3. A Database and Evaluation Methodology for Optical Flow
    2010 1.3k citations Daniel Scharstein, Richard Szeliski et al. profile →
  4. High-accuracy stereo depth maps using structured light
    2003 1.0k citations Daniel Scharstein, Richard Szeliski profile →
  5. Evaluation of Cost Functions for Stereo Matching
    2007 767 citations Heiko Hirschmüller, Daniel Scharstein profile →
  6. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms
    2002 713 citations Daniel Scharstein, Richard Szeliski et al. profile →
  7. A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors
    2008 643 citations Rick Szeliski, Ramin Zabih et al. IEEE Transactions on Pattern Analysis and Machine Intelligence profile →
  8. Learning Conditional Random Fields for Stereo
    2007 599 citations Daniel Scharstein, Chris Pal profile →
  9. Evaluation of Stereo Matching Costs on Images with Radiometric Differences
    2008 511 citations Heiko Hirschmüller, Daniel Scharstein IEEE Transactions on Pattern Analysis and Machine Intelligence profile →
  10. A Database and Evaluation Methodology for Optical Flow
    2007 394 citations Daniel Scharstein, Rick Szeliski et al. profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Daniel Scharstein 11.9k 3.0k 2.7k 1.1k 984 41 13.4k
Sing Bing Kang 9.3k 0.8× 2.3k 0.8× 1.5k 0.6× 1.8k 1.6× 631 0.6× 178 10.8k
Ramin Zabih 11.5k 1.0× 2.2k 0.7× 1.6k 0.6× 794 0.7× 453 0.5× 88 14.2k
Long Quan 6.6k 0.6× 1.2k 0.4× 2.6k 1.0× 1.1k 1.0× 1.3k 1.3× 359 10.5k
Roberto Cipolla 13.6k 1.1× 1.4k 0.5× 4.0k 1.5× 1.0k 0.9× 1.3k 1.3× 322 17.1k
Vladimir Kolmogorov 11.4k 1.0× 1.6k 0.5× 1.6k 0.6× 875 0.8× 396 0.4× 50 14.6k
Rick Szeliski 6.2k 0.5× 1.5k 0.5× 1.6k 0.6× 621 0.6× 378 0.4× 25 6.9k
In So Kweon 7.9k 0.7× 1.9k 0.6× 1.9k 0.7× 343 0.3× 460 0.5× 321 9.9k
Stefano Soatto 8.5k 0.7× 1.4k 0.5× 2.7k 1.0× 588 0.5× 473 0.5× 275 11.0k
Katsushi Ikeuchi 8.3k 0.7× 923 0.3× 2.1k 0.8× 2.7k 2.4× 1.2k 1.2× 498 11.9k
Narendra Ahuja 15.0k 1.3× 3.8k 1.3× 2.3k 0.8× 842 0.8× 249 0.3× 378 17.6k

Countries citing papers authored by Daniel Scharstein

Since Specialization
Citations

This map shows the geographic impact of Daniel Scharstein's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daniel Scharstein with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Scharstein more than expected).

Fields of papers citing papers by Daniel Scharstein

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Scharstein. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daniel Scharstein. The network helps show where Daniel Scharstein may publish in the future.

Co-authorship network of co-authors of Daniel Scharstein

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Scharstein. A scholar is included among the top collaborators of Daniel Scharstein based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Daniel Scharstein. Daniel Scharstein is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Huang, Chun-Hao P., et al.. (2022). Capturing and Inferring Dense Full-Body Human-Scene Contact. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 13264–13275. 66 indexed citations
2.
Xue, Tianfan, Andrew Owens, Daniel Scharstein, Michael Goesele, & Richard Szeliski. (2019). Multi-frame stereo matching with edges, planes, and superpixels. Image and Vision Computing. 91. 103771–103771. 18 indexed citations
3.
Sinha, Sudipta N., Daniel Scharstein, & Richard Szeliski. (2014). Efficient High-Resolution Stereo Matching Using Local Plane Sweeps. 1582–1589. 84 indexed citations
4.
Sinha, Sudipta N., Johannes Kopf, Michael Goesele, Daniel Scharstein, & Richard Szeliski. (2012). Image-based rendering for scenes with reflections. ACM Transactions on Graphics. 31(4). 1–10. 83 indexed citations
5.
Sinha, Sudipta N., Johannes Kopf, Michael Goesele, Daniel Scharstein, & Richard Szeliski. (2012). Image-based rendering for scenes with reflections. ACM Transactions on Graphics. 31(4). 1–10. 12 indexed citations
6.
Bleyer, Michael, Carsten Rother, Pushmeet Kohli, Daniel Scharstein, & Sudipta N. Sinha. (2011). Object stereo — Joint stereo matching and object segmentation. 3081–3088. 92 indexed citations
7.
Fouhey, David F., Daniel Scharstein, & Amy Briggs. (2010). Multiple Plane Detection in Image Pairs Using J-Linkage. 336–339. 18 indexed citations
8.
Szeliski, Rick, Ramin Zabih, Daniel Scharstein, et al.. (2008). 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. 643 indexed citations breakdown →
9.
Hirschmüller, Heiko & Daniel Scharstein. (2008). Evaluation of Stereo Matching Costs on Images with Radiometric Differences. IEEE Transactions on Pattern Analysis and Machine Intelligence. 31(9). 1582–1599. 511 indexed citations breakdown →
10.
Scharstein, Daniel & Chris Pal. (2007). Learning Conditional Random Fields for Stereo. 1–8. 599 indexed citations breakdown →
11.
Hirschmüller, Heiko & Daniel Scharstein. (2007). Evaluation of Cost Functions for Stereo Matching. 1–8. 767 indexed citations breakdown →
12.
Briggs, Amy, Carrick Detweiler, Yunpeng Li, Peter C. Mullen, & Daniel Scharstein. (2006). Matching scale-space features in 1D panoramas. Computer Vision and Image Understanding. 103(3). 184–195. 7 indexed citations
13.
Szeliski, Richard & Daniel Scharstein. (2004). Sampling the disparity space image. IEEE Transactions on Pattern Analysis and Machine Intelligence. 26(3). 419–425. 59 indexed citations
14.
Scharstein, Daniel. (2002). Matching images by comparing their gradient fields. 1. 572–575. 54 indexed citations
15.
Scharstein, Daniel & Amy Briggs. (2001). Real-time recognition of self-similar landmarks. Image and Vision Computing. 19(11). 763–772. 34 indexed citations
16.
Scharstein, Daniel. (1999). View Synthesis Using Stereo Vision. Lecture notes in computer science. 76 indexed citations
17.
Dickerson, Matthew T. & Daniel Scharstein. (1998). Optimal placement of convex polygons to maximize point containment. Computational Geometry. 11(1). 1–16. 11 indexed citations
18.
Dickerson, Matthew T. & Daniel Scharstein. (1996). Optimal placement of convex polygons to maximize point containment. Symposium on Discrete Algorithms. 114–121. 1 indexed citations
19.
Scharstein, Daniel & Richard Szeliski. (1996). Stereo matching with non-linear diffusion. 343–350. 68 indexed citations
20.
Segre, Alberto M. & Daniel Scharstein. (1993). Bounded-overhead caching for definite-clause theorem proving. Journal of Automated Reasoning. 11(1). 83–113. 8 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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