Chris Sweeney

781 total citations
20 papers, 437 citations indexed

About

Chris Sweeney is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Computational Mechanics. According to data from OpenAlex, Chris Sweeney has authored 20 papers receiving a total of 437 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computer Vision and Pattern Recognition, 14 papers in Aerospace Engineering and 2 papers in Computational Mechanics. Recurrent topics in Chris Sweeney's work include Advanced Vision and Imaging (13 papers), Robotics and Sensor-Based Localization (13 papers) and Advanced Image and Video Retrieval Techniques (9 papers). Chris Sweeney is often cited by papers focused on Advanced Vision and Imaging (13 papers), Robotics and Sensor-Based Localization (13 papers) and Advanced Image and Video Retrieval Techniques (9 papers). Chris Sweeney collaborates with scholars based in United States, Australia and Switzerland. Chris Sweeney's co-authors include Tobias Höllerer, Maryam Najafian, Matthew Turk, John P. Flynn, Marc Pollefeys, Torsten Sattler, Matthew Turk, Richard Newcombe, Minh Vo and Lingni Ma and has published in prestigious journals such as IEEE Transactions on Electron Devices, IEEE Transactions on Visualization and Computer Graphics and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

In The Last Decade

Chris Sweeney

20 papers receiving 422 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chris Sweeney United States 13 327 231 68 59 55 20 437
Patrick Sayd France 11 415 1.3× 258 1.1× 74 1.1× 19 0.3× 41 0.7× 20 490
Tristan Laidlow United Kingdom 6 280 0.9× 146 0.6× 77 1.1× 36 0.6× 75 1.4× 8 370
Vincent Gay‐Bellile France 10 288 0.9× 218 0.9× 63 0.9× 10 0.2× 34 0.6× 24 357
Nikhil Keetha United States 4 192 0.6× 149 0.6× 35 0.5× 40 0.7× 28 0.5× 7 305
L. Vacchetti Switzerland 6 462 1.4× 339 1.5× 104 1.5× 14 0.2× 21 0.4× 6 506
Gaku Narita Japan 5 224 0.7× 108 0.5× 70 1.0× 10 0.2× 63 1.1× 5 293
Oliver Wasenmüller Germany 9 189 0.6× 122 0.5× 53 0.8× 26 0.4× 29 0.5× 29 280
Scott Satkin United States 7 274 0.8× 111 0.5× 43 0.6× 38 0.6× 22 0.4× 11 321
Julien Pilet Switzerland 11 444 1.4× 247 1.1× 42 0.6× 15 0.3× 103 1.9× 17 492
Carl Yuheng Ren United Kingdom 7 381 1.2× 282 1.2× 149 2.2× 7 0.1× 49 0.9× 10 436

Countries citing papers authored by Chris Sweeney

Since Specialization
Citations

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

Fields of papers citing papers by Chris Sweeney

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chris Sweeney

This figure shows the co-authorship network connecting the top 25 collaborators of Chris Sweeney. A scholar is included among the top collaborators of Chris Sweeney 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 Chris Sweeney. Chris Sweeney 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.
Corona, Enric, Tomáš Hodaň, Minh Vo, et al.. (2022). LISA: Learning Implicit Shape and Appearance of Hands. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 20501–20511. 49 indexed citations
2.
Baráth, Dániel & Chris Sweeney. (2022). Relative Pose Solvers using Monocular Depth. 2022 26th International Conference on Pattern Recognition (ICPR). 31. 4037–4043. 1 indexed citations
3.
Wei, Fangyin, Lingni Ma, Christoph Lassner, et al.. (2022). Self-supervised Neural Articulated Shape and Appearance Models. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 15795–15805. 19 indexed citations
4.
DeTone, Daniel, Geoffrey Pascoe, Tanner Schmidt, et al.. (2022). Feature Query Networks: Neural Surface Description for Camera Pose Refinement. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 5067–5077. 9 indexed citations
5.
DeTone, Daniel, Tsun-Yi Yang, Tianwei Shen, et al.. (2022). NinjaDesc: Content-Concealing Visual Descriptors via Adversarial Learning. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 12787–12797. 16 indexed citations
6.
Li, Kejie, Daniel DeTone, Steven Chen, et al.. (2021). ODAM: Object Detection, Association, and Mapping using Posed RGB Video. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 5978–5988. 14 indexed citations
7.
Sweeney, Chris & Maryam Najafian. (2020). Reducing sentiment polarity for demographic attributes in word embeddings using adversarial learning. 359–368. 18 indexed citations
8.
Sweeney, Chris & Maryam Najafian. (2019). A Transparent Framework for Evaluating Unintended Demographic Bias in Word Embeddings. 1662–1667. 38 indexed citations
9.
Sweeney, Chris, et al.. (2019). A Supervised Approach to Predicting Noise in Depth Images. 796–802. 23 indexed citations
10.
Fragoso, Victor, et al.. (2017). GraphMatch: Efficient Large-Scale Graph Construction for Structure from Motion. 3. 165–174. 6 indexed citations
11.
Nuernberger, Benjamin, et al.. (2016). Multi-view gesture annotations in image-based 3D reconstructed scenes. 129–138. 19 indexed citations
12.
Kneip, Laurent, Chris Sweeney, & Richard Hartley. (2016). The generalized relative pose and scale problem: View-graph fusion via 2D-2D registration. ANU Open Research (Australian National University). 1–9. 10 indexed citations
13.
Sweeney, Chris, John P. Flynn, Benjamin Nuernberger, Matthew Turk, & Tobias Höllerer. (2015). Efficient Computation of Absolute Pose for Gravity-Aware Augmented Reality. 19–24. 28 indexed citations
14.
Sweeney, Chris, Torsten Sattler, Tobias Höllerer, Matthew Turk, & Marc Pollefeys. (2015). Optimizing the Viewing Graph for Structure-from-Motion. 801–809. 80 indexed citations
15.
Sweeney, Chris, Laurent Kneip, Tobias Höllerer, & Matthew Turk. (2015). Computing similarity transformations from only image correspondences. ANU Open Research (Australian National University). 3305–3313. 14 indexed citations
16.
Sweeney, Chris, John P. Flynn, & Matthew Turk. (2014). Solving for Relative Pose with a Partially Known Rotation is a Quadratic Eigenvalue Problem. 483–490. 45 indexed citations
17.
Sweeney, Chris, Tobias Höllerer, & Matthew Turk. (2013). Improved outdoor augmented reality through “Globalization”. 1–4. 3 indexed citations
18.
Gauglitz, Steffen, Chris Sweeney, Jonathan Ventura, Matthew Turk, & Tobias Höllerer. (2013). Model Estimation and Selection towardsUnconstrained Real-Time Tracking and Mapping. IEEE Transactions on Visualization and Computer Graphics. 20(6). 825–838. 12 indexed citations
19.
Gauglitz, Steffen, Chris Sweeney, Jonathan Ventura, Matthew Turk, & Tobias Höllerer. (2012). Live tracking and mapping from both general and rotation-only camera motion. 13–22. 30 indexed citations
20.
Peek, H.L., et al.. (1991). The tacking CCD: a new CCD concept. IEEE Transactions on Electron Devices. 38(5). 1193–1200. 3 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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