Fabio Tosi

2.3k total citations
43 papers, 998 citations indexed

About

Fabio Tosi is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Aerospace Engineering. According to data from OpenAlex, Fabio Tosi has authored 43 papers receiving a total of 998 indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Computer Vision and Pattern Recognition, 17 papers in Media Technology and 9 papers in Aerospace Engineering. Recurrent topics in Fabio Tosi's work include Advanced Vision and Imaging (36 papers), Optical measurement and interference techniques (18 papers) and Image Processing Techniques and Applications (17 papers). Fabio Tosi is often cited by papers focused on Advanced Vision and Imaging (36 papers), Optical measurement and interference techniques (18 papers) and Image Processing Techniques and Applications (17 papers). Fabio Tosi collaborates with scholars based in Italy, China and United States. Fabio Tosi's co-authors include Matteo Poggi, Stefano Mattoccia, Filippo Aleotti, Youmin Zhang, Luigi Di Stefano, Philippos Mordohai, Chaoqiang Zhao, Pierluigi Zama Ramirez, Samuele Salti and Andreas Geiger and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision and IEEE Transactions on Intelligent Transportation Systems.

In The Last Decade

Fabio Tosi

39 papers receiving 969 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fabio Tosi Italy 16 827 360 229 66 63 43 998
Yiping Gong China 6 575 0.7× 428 1.2× 191 0.8× 39 0.6× 86 1.4× 9 791
Gellért Máttyus Canada 11 789 1.0× 243 0.7× 254 1.1× 48 0.7× 381 6.0× 14 1.2k
Yongchao Gong China 7 627 0.8× 152 0.4× 83 0.4× 24 0.4× 75 1.2× 12 878
Qingwang Wang China 16 317 0.4× 455 1.3× 143 0.6× 93 1.4× 112 1.8× 67 797
Jirui Yang China 6 986 1.2× 491 1.4× 405 1.8× 14 0.2× 56 0.9× 10 1.2k
Huai Yu China 16 524 0.6× 215 0.6× 368 1.6× 34 0.5× 125 2.0× 50 888
Franz Kurz Germany 15 451 0.5× 224 0.6× 198 0.9× 75 1.1× 245 3.9× 72 843
V. Prinet China 15 378 0.5× 134 0.4× 57 0.2× 32 0.5× 115 1.8× 50 659
Konrad Schindler Switzerland 7 858 1.0× 211 0.6× 129 0.6× 93 1.4× 120 1.9× 12 1.1k

Countries citing papers authored by Fabio Tosi

Since Specialization
Citations

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

Fields of papers citing papers by Fabio Tosi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fabio Tosi

This figure shows the co-authorship network connecting the top 25 collaborators of Fabio Tosi. A scholar is included among the top collaborators of Fabio Tosi 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 Fabio Tosi. Fabio Tosi 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.
Tosi, Fabio, et al.. (2026). How NeRFs and 3-D Gaussian Splatting Are Reshaping SLAM: A Survey. IEEE Transactions on Robotics. 42. 1405–1427.
2.
3.
Tosi, Fabio, et al.. (2025). A Survey on Deep Stereo Matching in the Twenties. International Journal of Computer Vision. 133(7). 4245–4276. 7 indexed citations
4.
Tosi, Fabio, et al.. (2025). Stereo Anywhere: Robust Zero-Shot Deep Stereo Matching Even Where Either Stereo or Mono Fail. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 1013–1027. 2 indexed citations
5.
Tosi, Fabio, Filippo Aleotti, Pierluigi Zama Ramirez, et al.. (2024). Neural Disparity Refinement. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(12). 8900–8917. 4 indexed citations
6.
Poggi, Matteo & Fabio Tosi. (2024). Federated Online Adaptation for Deep Stereo. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 20165–20175. 4 indexed citations
7.
Qiao, Xin, Youmin Zhang, Yanhui Zhou, et al.. (2023). Depth super-resolution from explicit and implicit high-frequency features. Computer Vision and Image Understanding. 237. 103841–103841. 5 indexed citations
8.
Ramirez, Pierluigi Zama, Fabio Tosi, Matteo Poggi, et al.. (2023). Booster: A Benchmark for Depth From Images of Specular and Transparent Surfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(1). 85–102. 8 indexed citations
9.
Qiao, Xin, et al.. (2023). Self-supervised depth super-resolution with contrastive multiview pre-training. Neural Networks. 168. 223–236. 9 indexed citations
10.
Spencer, Jaime, Cheng Qian, Chris Russell, et al.. (2023). The Monocular Depth Estimation Challenge. ePrints Soton (University of Southampton). 623–632. 9 indexed citations
11.
Zhao, Chaoqiang, Matteo Poggi, Fabio Tosi, et al.. (2023). GasMono: Geometry-Aided Self-Supervised Monocular Depth Estimation for Indoor Scenes. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 16163–16174. 11 indexed citations
12.
Poggi, Matteo, Pierluigi Zama Ramirez, Fabio Tosi, et al.. (2022). Cross-Spectral Neural Radiance Fields. 606–616. 17 indexed citations
13.
Tosi, Fabio, Pierluigi Zama Ramirez, Matteo Poggi, et al.. (2022). RGB-Multispectral Matching: Dataset, Learning Methodology, Evaluation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 15937–15947. 5 indexed citations
14.
Tosi, Fabio, Filippo Aleotti, Matteo Poggi, et al.. (2021). On the Deployment of Out-of-the-Box Embedded Devices for Self-Powered River Surface Flow Velocity Monitoring at the Edge. Applied Sciences. 11(15). 7027–7027. 5 indexed citations
15.
Tosi, Fabio, et al.. (2021). SMD-Nets: Stereo Mixture Density Networks. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 59 indexed citations
16.
Poggi, Matteo, et al.. (2021). On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images: a Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(9). 1–1. 90 indexed citations
17.
Poggi, Matteo, Gianluca Agresti, Fabio Tosi, Pietro Zanuttigh, & Stefano Mattoccia. (2019). Confidence Estimation for ToF and Stereo Sensors and Its Application to Depth Data Fusion. IEEE Sensors Journal. 20(3). 1411–1421. 18 indexed citations
18.
Calimera, Andrea, et al.. (2019). Enabling Energy-Efficient Unsupervised Monocular Depth Estimation on ARMv7-Based Platforms. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 1703–1708. 19 indexed citations
19.
Tosi, Fabio, et al.. (2018). Virtual Simulation for Clutch Thermal Behavior Prediction. SAE technical papers on CD-ROM/SAE technical paper series. 1. 3 indexed citations
20.
Poggi, Matteo, Fabio Tosi, & Stefano Mattoccia. (2017). Even More Confident Predictions with Deep Machine-Learning. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 393–401. 7 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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