Daniel Erickson

530 citations
10 papers · 381 · h-index 7

Impact in

Papers in

Daniel Erickson

8 papers receiving 366 citations

Peers

Daniel Erickson
Comparison fields: 5 of 37
  • Computer Graphics and Computer-Aided Design 188
  • Computer Vision and Pattern Recognition 350
  • Media Technology 44
  • Human-Computer Interaction 21
  • Computational Mechanics 59
Replace Jason Dourgarian with:
Jason Dourgarian United States
Matthew DuVall United States
T. Takai Japan
Anita Sellent Germany
Abhimitra Meka United States
Siegfried Föessel Germany
YiChang Shih United States
Daniel Cotting Switzerland
Jean‐Marc Hasenfratz France
Zhewei Huang China
Daniel Erickson relative to Jason Dourgarian United States Jason Dourgarian's profile →
Citations per field
00.5×4.1×
Jason Dourgarian · 1×
Citations per year

Countries citing papers authored by Daniel Erickson

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Erickson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Daniel Erickson, 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 Daniel Erickson Line = papers co-authored together Daniel Erickson links everyone, so they are left out of the graph.

All Works

10 of 10 papers shown
#Work
1 2020189
2 2018112
3
Deep Relightable Textures Volumetric Performance Capture with Neural Rendering
202043
4 201810
5 20189
6 20209
7 20197
8 20032
9 20020
10 20090

About Daniel Erickson

Daniel Erickson is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design, Artificial Intelligence, Media Technology and Sociology and Political Science, having authored 10 papers that have together received 381 indexed citations. Recurring topics across this work include Advanced Vision and Imaging (6 papers), Computer Graphics and Visualization Techniques (6 papers), Image and Signal Denoising Methods (2 papers), Neural Networks and Applications (2 papers), Advanced Data Compression Techniques (2 papers), Advanced Optical Imaging Technologies (2 papers), Advanced Image Processing Techniques (1 paper) and Digital Games and Media (1 paper). The work is most often cited by research in Computer Graphics and Computer-Aided Design (188 citations), Computer Vision and Pattern Recognition (350 citations), Media Technology (44 citations), Human-Computer Interaction (21 citations) and Computational Mechanics (59 citations). Daniel Erickson has collaborated with scholars based in United States. Frequent co-authors include Paul Debevec, Ryan Overbeck, Matt Pharr, Peter Hedman, Matt Whalen, Jason Dourgarian, Michael Broxton, Jay Busch, Matthew DuVall and John P. Flynn. Their work appears in journals such as ACM Transactions on Graphics and MPG.PuRe (Max Planck Society).

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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