Jeremy Reizenstein

3.5k citations
6 papers · 546 indexed · 2 hit papers · h-index 5

Jeremy Reizenstein

6 papers receiving 524 citations

Hit Papers

Common Objects in 3D: Large-Scale Learning and Evaluation...1832020202620222024100200300

Peers

Jeremy Reizenstein
Comparison fields: 5 of 73
  • Computer Graphics and Computer-Aided Design 174
  • Computer Vision and Pattern Recognition 416
  • Geology 77
  • Computational Mechanics 236
  • Aerospace Engineering 90
Replace George Leifman with:
George Leifman Israel
Rundi Wu United States
Roland Angst Switzerland
Chao-Hui Shen China
Kwan-Yee Lin China
Michael Niemeyer United States
Matheus Gadelha United States
Jean‐Yves Guillemaut United Kingdom
Alexander Berner Germany
Nikhil Gagvani United States
Jeremy Reizenstein relative to George Leifman Israel George Leifman's profile →
Citations per field
00.5×11.4×
George Leifman · 1×
Citations per year

Countries citing papers authored by Jeremy Reizenstein

Since Specialization
Citations

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

Fields of papers citing papers by Jeremy Reizenstein

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 17 scholars most cited alongside Jeremy Reizenstein, 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 Jeremy Reizenstein Line = papers co-authored together Jeremy Reizenstein links everyone, so they are left out of the graph.

All Works

6 of 6 papers shown
#Work
1 20237
2 20231
3
Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstructionbreakdown →
2021183
4
Accelerating 3D deep learning with PyTorch3Dbreakdown →
2020334
5 20207
6
PerspectiveNet: A Scene-consistent Image Generator for New View Synthesis in Real Indoor Environments
201914

About Jeremy Reizenstein

Jeremy Reizenstein is a scholar working on Discrete Mathematics and Combinatorics, Computer Graphics and Computer-Aided Design and Computer Vision and Pattern Recognition, having authored 6 papers that have together received 546 indexed citations. Recurring topics across this work include Advanced Vision and Imaging (3 papers), 3D Shape Modeling and Analysis (2 papers), Advanced Image and Video Retrieval Techniques (1 paper), Polynomial and algebraic computation (1 paper), Advanced Combinatorial Mathematics (1 paper), Music and Audio Processing (1 paper), Computer Graphics and Visualization Techniques (1 paper) and Image Processing and 3D Reconstruction (1 paper). The work is most often cited by research in Computer Graphics and Computer-Aided Design (174 citations), Computer Vision and Pattern Recognition (416 citations) and Geology (77 citations). Jeremy Reizenstein has collaborated with scholars based in United Kingdom, Israel and Canada. Frequent co-authors include David Novotný, Justin Johnson, Steve Branson, Shubham Tulsiani, Nikhila Ravi, Christoph Lassner, Роман Шаповалов, Philipp Henzler, L. Sbordone and Patrick Labatut. Their work appears in journals such as ACM Transactions on Mathematical Software, Journal of Algebra and 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

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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