Kartic Subr

958 citations
45 papers · 594 · h-index 14

Impact in

Papers in

Kartic Subr

43 papers receiving 574 citations

Peers

Kartic Subr
Comparison fields: 5 of 70
  • Computer Graphics and Computer-Aided Design 245
  • Computer Vision and Pattern Recognition 375
  • Computational Mechanics 128
  • Media Technology 53
  • Oceanography 44
Replace Yakun Ju with:
Yakun Ju China
Yi Wei China
Tiow-Seng Tan Singapore
Florence Denis France
Zhengqi Li United States
André Gagalowicz France
José Gabriel R. C. Gomes Brazil
Craig Kolb United States
Andrés Romero United States
Kei Iwasaki Japan
Kartic Subr relative to Yakun Ju China Yakun Ju's profile →
Citations per field
00.5×7.8×
Yakun Ju · 1×
Citations per year

Countries citing papers authored by Kartic Subr

Since Specialization
Citations

This map shows the geographic impact of Kartic Subr'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 Kartic Subr with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kartic Subr more than expected).

Fields of papers citing papers by Kartic Subr

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kartic Subr. 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 Kartic Subr. The network helps show where Kartic Subr may publish in the future.

Co-authors

The 25 scholars most cited alongside Kartic Subr, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Kartic Subr Line = papers co-authored together Kartic Subr links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 45 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200978
2 201867
3 201349
4 201334
5 201130
6 201026
7 201125
8 201323
9 201321
10 202118
11 202017
12
Adaptable Pouring: Teaching Robots Not to Spill using Fast but Approximate Fluid Simulation
201716
13 201815
14 201414
15 201912
16 200912
17 201111
18 202310
19 202010
20 20159

About Kartic Subr

Kartic Subr is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design, Computational Mechanics, Media Technology and Aerospace Engineering, having authored 45 papers that have together received 594 indexed citations. Recurring topics across this work include Computer Graphics and Visualization Techniques (16 papers), Advanced Vision and Imaging (13 papers), 3D Shape Modeling and Analysis (10 papers), Robotics and Sensor-Based Localization (5 papers), Advanced Image Processing Techniques (4 papers), Optical measurement and interference techniques (4 papers), Data Visualization and Analytics (3 papers) and Image Processing Techniques and Applications (3 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (245 citations), Computer Vision and Pattern Recognition (375 citations), Computational Mechanics (128 citations), Media Technology (53 citations) and Oceanography (44 citations). Kartic Subr has collaborated with scholars based in United Kingdom, United States and France. Frequent co-authors include Cyril Soler, Nicolas Holzschuch, Jan Kautz, Frédo Durand, Yvan Pétillot, François X. Sillion, Laurent Belcour, Ravi Ramamoorthi, Derek Nowrouzezahrai and Fabrizio Pece. Their work appears in journals such as Computer Graphics Forum, ACM Transactions on Graphics, IEEE Transactions on Visualization and Computer Graphics, IEEE Robotics and Automation Letters and Bioinformatics.

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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