Peter Wonka

15.0k total citations · 3 hit papers
201 papers, 9.3k citations indexed

About

Peter Wonka is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Computational Mechanics. According to data from OpenAlex, Peter Wonka has authored 201 papers receiving a total of 9.3k indexed citations (citations by other indexed papers that have themselves been cited), including 117 papers in Computer Vision and Pattern Recognition, 94 papers in Computer Graphics and Computer-Aided Design and 66 papers in Computational Mechanics. Recurrent topics in Peter Wonka's work include Computer Graphics and Visualization Techniques (82 papers), 3D Shape Modeling and Analysis (54 papers) and Advanced Vision and Imaging (49 papers). Peter Wonka is often cited by papers focused on Computer Graphics and Visualization Techniques (82 papers), 3D Shape Modeling and Analysis (54 papers) and Advanced Vision and Imaging (49 papers). Peter Wonka collaborates with scholars based in United States, Saudi Arabia and Canada. Peter Wonka's co-authors include Jieping Ye, Przemysław Musialski, Pascal Müller, Ji Liu, Luc Van Gool, Michael Wimmer, Simon Haegler, François X. Sillion, William Ribarsky and Liangliang Nan and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Geoscience and Remote Sensing and ACM Transactions on Graphics.

In The Last Decade

Peter Wonka

198 papers receiving 9.0k citations

Hit Papers

Tensor Completion for Est... 2006 2026 2012 2019 2012 2006 2013 400 800 1.2k

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Peter Wonka 4.2k 2.9k 2.6k 2.2k 1.8k 201 9.3k
Yulan Guo 5.8k 1.4× 2.7k 1.0× 546 0.2× 2.9k 1.3× 2.3k 1.3× 168 9.6k
Roberto Cipolla 13.6k 3.2× 998 0.3× 1.0k 0.4× 1.3k 0.6× 732 0.4× 322 17.1k
Shiming Xiang 5.8k 1.4× 1.0k 0.4× 265 0.1× 545 0.2× 948 0.5× 232 9.4k
Jean Ponce 18.7k 4.4× 3.2k 1.1× 949 0.4× 1.9k 0.9× 1.4k 0.8× 172 24.8k
Chunhong Pan 5.7k 1.3× 928 0.3× 288 0.1× 588 0.3× 1.1k 0.6× 311 9.7k
Katsushi Ikeuchi 8.3k 2.0× 1.6k 0.6× 2.7k 1.0× 1.2k 0.5× 529 0.3× 498 11.9k
Wenping Wang 3.4k 0.8× 3.9k 1.3× 3.0k 1.2× 681 0.3× 409 0.2× 418 9.3k
Raquel Urtasun 15.9k 3.8× 1.3k 0.5× 577 0.2× 1.4k 0.6× 2.3k 1.3× 191 21.2k
Claudio Silva 2.8k 0.7× 2.1k 0.7× 2.3k 0.9× 562 0.3× 453 0.3× 224 6.5k
Greg Turk 5.8k 1.4× 6.2k 2.2× 6.9k 2.7× 736 0.3× 408 0.2× 128 11.7k

Countries citing papers authored by Peter Wonka

Since Specialization
Citations

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

Fields of papers citing papers by Peter Wonka

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Peter Wonka

This figure shows the co-authorship network connecting the top 25 collaborators of Peter Wonka. A scholar is included among the top collaborators of Peter Wonka 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 Peter Wonka. Peter Wonka is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Chandrasekaran, Arun Pandian, Yingzi Zhang, Mengge Wang, et al.. (2025). deepBlastoid: a deep learning model for automated and efficient evaluation of human blastoids. PubMed. 4(6). lnaf026–lnaf026.
2.
Fan, Yue, et al.. (2025). Factored-NeuS: Reconstructing Surfaces, Illumination, and Materials of Possibly Glossy Objects. 21317–21327. 1 indexed citations
3.
Pó, Riccardo, Yifan Wang, Vladislav Golyanik, et al.. (2024). State of the Art on Diffusion Models for Visual Computing. Computer Graphics Forum. 43(2). 36 indexed citations
4.
Quan, Weize, J. Chen, Yanli Liu, Dong‐Ming Yan, & Peter Wonka. (2024). Deep Learning-Based Image and Video Inpainting: A Survey. International Journal of Computer Vision. 132(7). 2367–2400. 23 indexed citations
5.
Li, Zhenyu, Shariq Farooq Bhat, & Peter Wonka. (2024). PatchFusion: An End-to-End Tile-Based Framework for High-Resolution Monocular Metric Depth Estimation. 10016–10025. 8 indexed citations
6.
Kelly, Tom, John Femiani, & Peter Wonka. (2024). WinSyn: A High Resolution Testbed for Synthetic Data. 22456–22465. 2 indexed citations
7.
Gilmutdinov, I.F., et al.. (2022). Assessment of Material Layers in Building Walls Using GeoRadar. Remote Sensing. 14(19). 5038–5038. 2 indexed citations
8.
Strnad, Ondřej, et al.. (2022). Finding Nano-Ötzi: Cryo-Electron Tomography Visualization Guided by Learned Segmentation. IEEE Transactions on Visualization and Computer Graphics. 29(10). 4198–4214. 7 indexed citations
9.
Zou, Chuhang, et al.. (2021). Manhattan Room Layout Reconstruction from a Single $$360^{\circ }$$ Image: A Comparative Study of State-of-the-Art Methods. International Journal of Computer Vision. 129(5). 1410–1431. 35 indexed citations
10.
Wonka, Peter, et al.. (2021). Flow-Guided Video Inpainting with Scene Templates. King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology). 5 indexed citations
11.
Qin, Yipeng, Niloy J. Mitra, & Peter Wonka. (2018). Do GAN Loss Functions Really Matter. arXiv (Cornell University). 1 indexed citations
12.
Nan, Liangliang & Peter Wonka. (2017). PolyFit: Polygonal Surface Reconstruction from Point Clouds. King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology). 2372–2380. 178 indexed citations
13.
Wang, Jie, Qingyang Li, Sen Yang, et al.. (2014). A Highly Scalable Parallel Algorithm for Isotropic Total Variation Models. International Conference on Machine Learning. 235–243. 11 indexed citations
14.
Liu, Ji, Peter Wonka, & Jieping Ye. (2012). A multi-stage framework for Dantzig selector and LASSO. Journal of Machine Learning Research. 13(1). 1189–1219. 5 indexed citations
15.
Kobayashi, Yoshihiro & Peter Wonka. (2011). Irregular vertex editing for architectural geometry design. Annual Simulation Symposium. 156–163. 1 indexed citations
16.
Liu, Ji, Ji Liu, Jun Liu, et al.. (2011). Sparse non-negative tensor factorization using columnwise coordinate descent. Pattern Recognition. 45(1). 649–656. 40 indexed citations
17.
Liu, Ji, Peter Wonka, & Jieping Ye. (2010). Multi-Stage Dantzig Selector. Neural Information Processing Systems. 23. 1450–1458. 5 indexed citations
18.
Wonka, Peter, et al.. (2009). Parallel generation of L-systems. Vision Modeling and Visualization. 205–214. 3 indexed citations
19.
Müller, Pascal, et al.. (2006). Procedural modeling of buildings. ACM Transactions on Graphics. 25(3). 614–623. 665 indexed citations breakdown →
20.
Bittner, Jiřı́, Peter Wonka, & Michael Wimmer. (2005). Fast exact from-region visibility in urban scenes. 223–230. 11 indexed citations

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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