Enric Corona
- Computer Vision and Pattern Recognition top 1%
- Computational Mechanics top 2%
- Computer Graphics and Computer-Aided Design top 0.5%
- Control and Systems Engineering top 5%
- Biomedical Engineering
- Co-authors
- Francesc Moreno-NoguerAlbert PumarolaGerard Pons‐MollGuillem AlenyàGrégory RogezCarme TorrasMinh VoLingni Ma
- Topics
- Human Pose and Action Recognition (6 papers)3D Shape Modeling and Analysis (6 papers)Advanced Vision and Imaging (4 papers)
- Cited by
- Computer Graphics and Computer-Aided DesignComputer Vision and Pattern RecognitionComputational Mechanics
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligencePattern Recognition2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Partner nations
- SpainUnited StatesGermany
In The Last Decade
Enric Corona
11 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 63
- Computer Vision and Pattern Recognition 893
- Computational Mechanics 486
- Computer Graphics and Computer-Aided Design 434
- Control and Systems Engineering 245
- Biomedical Engineering 98
Countries citing papers authored by Enric Corona
This map shows the geographic impact of Enric Corona'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 Enric Corona with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Enric Corona more than expected).
Fields of papers citing papers by Enric Corona
This network shows the impact of papers produced by Enric Corona. 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 Enric Corona. The network helps show where Enric Corona may publish in the future.
Co-authorship network of co-authors of Enric Corona
This figure shows the co-authorship network connecting the top 25 collaborators of Enric Corona. A scholar is included among the top collaborators of Enric Corona 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 Enric Corona. Enric Corona is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 2 | |
| 3 | 1 | |
| 4 | 12 | |
| 5 | 15 | |
| 6 | 49 | |
| 7 | D-NeRF: neural radiance fields for dynamic scenesbreakdown → | 697 |
| 8 | 110 | |
| 9 | 84 | |
| 10 | 112 | |
| 11 | 59 |
About Enric Corona
Enric Corona is a scholar working on Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition and Computational Mechanics, having authored 11 papers that have together received 1.1k indexed citations. Recurring topics across this work include Human Pose and Action Recognition (6 papers), 3D Shape Modeling and Analysis (6 papers) and Advanced Vision and Imaging (4 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (434 citations), Computer Vision and Pattern Recognition (893 citations) and Computational Mechanics (486 citations). Enric Corona has collaborated with scholars based in Spain, United States and Germany. Frequent co-authors include Francesc Moreno-Noguer, Albert Pumarola, Gerard Pons‐Moll, Guillem Alenyà, Grégory Rogez, Carme Torras, Minh Vo, Lingni Ma, Tomáš Hodaň and Chris Sweeney. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
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.