Daniel Erickson

520 citations
9 papers · 350 indexed · h-index 7

Impact in

Papers in

Daniel Erickson

8 papers receiving 334 citations

Peers

Daniel Erickson
Comparison fields: 5 of 34
  • Computer Graphics and Computer-Aided Design 175
  • Computer Vision and Pattern Recognition 323
  • Media Technology 37
  • Human-Computer Interaction 17
  • Computational Mechanics 56
Replace Jason Dourgarian with:
Jason Dourgarian United States
Matthew DuVall United States
Janne Kontkanen United States
T. Takai Japan
Jiamin Bai United States
John Isidoro United States
Abhimitra Meka United States
Anita Sellent Germany
YiChang Shih United States
Daniel Cotting Switzerland
Daniel Erickson relative to Jason Dourgarian United States Jason Dourgarian's profile →
Citations per field
00.5×4.4×
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

9 of 9 papers shown
#Work
1 2020173
2 2018106
3
Deep Relightable Textures Volumetric Performance Capture with Neural Rendering
202036
4 201810
5 20209
6 20188
7 20197
8 20031
9 20020

About Daniel Erickson

Daniel Erickson is a scholar working on Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition, Media Technology, Signal Processing and Artificial Intelligence, having authored 9 papers that have together received 350 indexed citations. Recurring topics across this work include Computer Graphics and Visualization Techniques (6 papers), Advanced Vision and Imaging (6 papers), Advanced Optical Imaging Technologies (2 papers), Neural Networks and Applications (2 papers), Image and Signal Denoising Methods (2 papers), Advanced Data Compression Techniques (2 papers), Video Coding and Compression Technologies (1 paper) and 3D Shape Modeling and Analysis (1 paper). The work is most often cited by research in Computer Graphics and Computer-Aided Design (175 citations), Computer Vision and Pattern Recognition (323 citations), Media Technology (37 citations), Human-Computer Interaction (17 citations) and Computational Mechanics (56 citations). Daniel Erickson has collaborated with scholars based in United States. Frequent co-authors include Paul Debevec, Ryan Overbeck, Matt Pharr, Jason Dourgarian, Michael Broxton, Jay Busch, Matthew DuVall, Matt Whalen, John P. Flynn and Peter Hedman. 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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