Yannick Verdié

899 total citations
11 papers, 347 citations indexed

About

Yannick Verdié is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Geology. According to data from OpenAlex, Yannick Verdié has authored 11 papers receiving a total of 347 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Computer Vision and Pattern Recognition, 6 papers in Aerospace Engineering and 4 papers in Geology. Recurrent topics in Yannick Verdié's work include Robotics and Sensor-Based Localization (6 papers), Advanced Vision and Imaging (5 papers) and 3D Surveying and Cultural Heritage (4 papers). Yannick Verdié is often cited by papers focused on Robotics and Sensor-Based Localization (6 papers), Advanced Vision and Imaging (5 papers) and 3D Surveying and Cultural Heritage (4 papers). Yannick Verdié collaborates with scholars based in Austria, Switzerland and Sweden. Yannick Verdié's co-authors include Florent Lafarge, Pierre Alliez, Pascal Fua, Kwang Moo Yi, Vincent Lepetit, Mahdi Rad, Aleš Leonardis, Jifei Song, Steven McDonagh and Benjamin Busam and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, ACM Transactions on Graphics and International Journal of Computer Vision.

In The Last Decade

Yannick Verdié

11 papers receiving 336 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Yannick Verdié Austria	7	209	151	136	121	41	11	347
E. K. Stathopoulou Greece	13	202 1.0×	130 0.9×	251 1.8×	142 1.2×	20 0.5×	26	416
Christian Früh United States	5	241 1.2×	206 1.4×	215 1.6×	199 1.6×	55 1.3×	5	425
Raoul Wessel Germany	8	137 0.7×	128 0.8×	303 2.2×	245 2.0×	90 2.2×	20	433
Tim Caselitz Germany	6	240 1.1×	281 1.9×	103 0.8×	86 0.7×	9 0.2×	9	385
Ali Hosseininaveh Ahmadabadian Iran	13	208 1.0×	172 1.1×	325 2.4×	169 1.4×	49 1.2×	30	449
Anh Nguyen Japan	2	111 0.5×	78 0.5×	172 1.3×	173 1.4×	19 0.5×	3	314
Julian Straub United States	11	277 1.3×	196 1.3×	103 0.8×	51 0.4×	9 0.2×	15	381
Jan Quenzel Germany	8	154 0.7×	170 1.1×	75 0.6×	70 0.6×	8 0.2×	16	260
Jian Dou China	4	177 0.8×	92 0.6×	88 0.6×	117 1.0×	15 0.4×	8	306
Branislav Mičušík Austria	13	395 1.9×	207 1.4×	103 0.8×	72 0.6×	11 0.3×	24	474

Countries citing papers authored by Yannick Verdié

Since Specialization

Citations

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

Fields of papers citing papers by Yannick Verdié

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yannick Verdié

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

All Works

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11 of 11 papers shown

1.

Jung, Hyunjun, Guangyao Zhai, Yitong Li, et al.. (2023). On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks. 780–791. 6 indexed citations

2.

Ng, Pai Chet, Yannick Verdié, Juwei Lu, & Konstantinos N. Plataniotis. (2022). Efficient Hyperspectral Reconstruction from RGB Images with Line-Pixel Deconvolution. Figshare. 1–5. 1 indexed citations

3.

Verdié, Yannick, et al.. (2022). CroMo: Cross-Modal Learning for Monocular Depth Estimation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 3927–3937. 12 indexed citations

4.

Rad, Mahdi, et al.. (2017). Robust 3D Object Tracking from Monocular Images Using Stable Parts. IEEE Transactions on Pattern Analysis and Machine Intelligence. 40(6). 1465–1479. 50 indexed citations

5.

Yi, Kwang Moo, Yannick Verdié, Pascal Fua, & Vincent Lepetit. (2016). Learning to Assign Orientations to Feature Points. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 107–116. 71 indexed citations

6.

Rad, Mahdi, et al.. (2015). A Novel Representation of Parts for Accurate 3D Object Detection and Tracking in Monocular Images. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 4391–4399. 47 indexed citations

7.

Verdié, Yannick, Florent Lafarge, & Pierre Alliez. (2015). LOD Generation for Urban Scenes. ACM Transactions on Graphics. 34(3). 1–14. 136 indexed citations

8.

Verdié, Yannick, et al.. (2014). [DEMO] Tracking texture-less, shiny objects with descriptor fields. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 331–332. 2 indexed citations

9.

Verdié, Yannick & Florent Lafarge. (2013). Detecting parametric objects in large scenes by Monte Carlo sampling. International Journal of Computer Vision. 106(1). 57–75. 19 indexed citations

10.

Verdié, Yannick, Florent Lafarge, & Josiane Zerubia. (2011). Generating compact meshes under planar constraints: An automatic approach for modeling buildings from aerial LiDAR. 60. 877–880. 1 indexed citations

11.

Verdié, Yannick, et al.. (2009). MirrorTrack. 347–350. 2 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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