Rareş Ambruş

1.7k total citations
39 papers, 651 citations indexed

About

Rareş Ambruş is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Media Technology. According to data from OpenAlex, Rareş Ambruş has authored 39 papers receiving a total of 651 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Computer Vision and Pattern Recognition, 14 papers in Aerospace Engineering and 7 papers in Media Technology. Recurrent topics in Rareş Ambruş's work include Robotics and Sensor-Based Localization (14 papers), Advanced Vision and Imaging (11 papers) and Optical measurement and interference techniques (8 papers). Rareş Ambruş is often cited by papers focused on Robotics and Sensor-Based Localization (14 papers), Advanced Vision and Imaging (11 papers) and Optical measurement and interference techniques (8 papers). Rareş Ambruş collaborates with scholars based in United States, Sweden and Germany. Rareş Ambruş's co-authors include Adrien Gaidon, Vitor Guizilini, Patric Jensfelt, John Folkesson, Sebastian Claici, Dian Chen, Nils Bore, Katerina Fragkiadaki, Adam W. Harley and Zhaoyuan Fang and has published in prestigious journals such as Robotics and Autonomous Systems, IEEE Robotics and Automation Letters and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

In The Last Decade

Rareş Ambruş

35 papers receiving 623 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Rareş Ambruş United States 16 476 262 108 89 77 39 651
Yohann Cabon South Korea 6 615 1.3× 231 0.9× 76 0.7× 83 0.9× 67 0.9× 11 776
Clemens Arth Austria 15 608 1.3× 416 1.6× 112 1.0× 89 1.0× 29 0.4× 51 754
Anton Konushin Russia 13 446 0.9× 98 0.4× 66 0.6× 54 0.6× 50 0.6× 63 640
Divyansh Garg United States 3 743 1.6× 415 1.6× 59 0.5× 102 1.1× 80 1.0× 4 839
Zhaozheng Hu China 14 273 0.6× 206 0.8× 66 0.6× 59 0.7× 78 1.0× 63 687
Yufeng Yue China 17 519 1.1× 511 2.0× 73 0.7× 29 0.3× 61 0.8× 85 844
Yue Qi China 11 409 0.9× 152 0.6× 52 0.5× 212 2.4× 40 0.5× 103 681
Ziyu Zhang China 9 761 1.6× 316 1.2× 53 0.5× 47 0.5× 83 1.1× 14 865
Cüneyt Akınlar Türkiye 12 876 1.8× 389 1.5× 110 1.0× 225 2.5× 127 1.6× 39 1.1k
Benjamin Busam Germany 16 422 0.9× 245 0.9× 112 1.0× 42 0.5× 48 0.6× 52 706

Countries citing papers authored by Rareş Ambruş

Since Specialization
Citations

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

Fields of papers citing papers by Rareş Ambruş

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rareş Ambruş

This figure shows the co-authorship network connecting the top 25 collaborators of Rareş Ambruş. A scholar is included among the top collaborators of Rareş Ambruş 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 Rareş Ambruş. Rareş Ambruş 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.
2.
Liu, Katherine, Vitor Guizilini, Takuya Ikeda, et al.. (2025). ZeroGrasp: Zero-Shot Shape Reconstruction Enabled Robotic Grasping. 17405–17415. 1 indexed citations
3.
4.
Liu, Katherine, Dian Chen, Takuya Ikeda, et al.. (2025). OmniShape: Zero-Shot Multi-Hypothesis Shape and Pose Estimation in the Real World. 16020–16027.
5.
Guizilini, Vitor, et al.. (2024). Transcrib3D: 3D Referring Expression Resolution through Large Language Models. 9737–9744. 2 indexed citations
6.
Kowal, Matthew, Achal Dave, Rareş Ambruş, et al.. (2024). Understanding Video Transformers via Universal Concept Discovery. 10946–10956. 1 indexed citations
7.
Chen, Dian, et al.. (2024). FSD: Fast Self-Supervised Single RGB-D to Categorical 3D Objects. 14630–14637. 4 indexed citations
8.
9.
Ambruş, Rareş, et al.. (2023). ShaSTA: Modeling Shape and Spatio-Temporal Affinities for 3D Multi-Object Tracking. IEEE Robotics and Automation Letters. 9(5). 4273–4280. 17 indexed citations
10.
Guizilini, Vitor, et al.. (2023). Towards Zero-Shot Scale-Aware Monocular Depth Estimation. 9199–9209. 29 indexed citations
11.
Guizilini, Vitor, et al.. (2023). Robust Self-Supervised Extrinsic Self-Calibration. 1932–1939. 3 indexed citations
12.
Guizilini, Vitor, et al.. (2022). Full Surround Monodepth From Multiple Cameras. IEEE Robotics and Automation Letters. 7(2). 5397–5404. 39 indexed citations
13.
Guizilini, Vitor, Kuan-Hui Lee, Rareş Ambruş, & Adrien Gaidon. (2022). Learning Optical Flow, Depth, and Scene Flow Without Real-World Labels. IEEE Robotics and Automation Letters. 7(2). 3491–3498. 32 indexed citations
14.
Ambruş, Rareş, Dennis Park, Wadim Kehl, et al.. (2021). Single-Shot Scene Reconstruction. 5 indexed citations
15.
Guizilini, Vitor, Rui Hou, Jie Li, Rareş Ambruş, & Adrien Gaidon. (2020). Semantically-Guided Representation Learning for Self-Supervised Monocular Depth. arXiv (Cornell University). 18 indexed citations
16.
Guizilini, Vitor, Rareş Ambruş, Sudeep Pillai, & Adrien Gaidon. (2019). PackNet-SfM: 3D Packing for Self-Supervised Monocular Depth Estimation.. arXiv (Cornell University). 10 indexed citations
17.
Tang, Jiexiong, et al.. (2019). Neural Outlier Rejection for Self-Supervised Keypoint Learning. arXiv (Cornell University). 2 indexed citations
18.
Krajník, Tomáš, et al.. (2015). Where's waldo at time t ? using spatio-temporal models for mobile robot search. Lincoln Repository (University of Lincoln). 2140–2146. 20 indexed citations
19.
Ambruş, Rareş, et al.. (2013). Unmanned Vehicles Conversion Kits. 1–6. 1 indexed citations
20.
Birk, Andreas, et al.. (2008). Terrain Classification for Autonomous Robot Mobility : from Safety, Security Rescue Robotics to Planetary Exploration. International Conference on Robotics and Automation. 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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