Stefan Milz

841 total citations
10 papers, 191 citations indexed

About

Stefan Milz is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Cognitive Neuroscience. According to data from OpenAlex, Stefan Milz has authored 10 papers receiving a total of 191 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computer Vision and Pattern Recognition, 5 papers in Aerospace Engineering and 2 papers in Cognitive Neuroscience. Recurrent topics in Stefan Milz's work include Robotics and Sensor-Based Localization (5 papers), Advanced Neural Network Applications (4 papers) and Neural dynamics and brain function (2 papers). Stefan Milz is often cited by papers focused on Robotics and Sensor-Based Localization (5 papers), Advanced Neural Network Applications (4 papers) and Neural dynamics and brain function (2 papers). Stefan Milz collaborates with scholars based in Germany, France and Spain. Stefan Milz's co-authors include Senthil Yogamani, Patrick Mäder, Georg Arbeiter, Kai Fischer, Martín Simón, Martin Rabe, V. Vaquero, Alberto Sanfeliu, Francesc Moreno-Noguer and Peter Schall and has published in prestigious journals such as Scientific Data, arXiv (Cornell University) and 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).

In The Last Decade

Stefan Milz

10 papers receiving 181 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Stefan Milz Germany	6	93	90	44	41	33	10	191
Qinghao Meng China	6	177 1.9×	57 0.6×	26 0.6×	31 0.8×	32 1.0×	11	234
Carlos Vallespi-Gonzalez United States	4	98 1.1×	41 0.5×	41 0.9×	31 0.8×	43 1.3×	7	197
Rohit Mohan India	8	166 1.8×	46 0.5×	22 0.5×	52 1.3×	34 1.0×	24	257
Jean Lahoud United Arab Emirates	4	169 1.8×	87 1.0×	52 1.2×	27 0.7×	38 1.2×	8	236
Yifeng Lu China	3	231 2.5×	129 1.4×	43 1.0×	43 1.0×	27 0.8×	6	316
Andrew J. Davison United Kingdom	6	127 1.4×	129 1.4×	34 0.8×	20 0.5×	14 0.4×	13	221
Haiyue Wei China	7	183 2.0×	97 1.1×	26 0.6×	25 0.6×	17 0.5×	8	268
Riku Murai United Kingdom	4	102 1.1×	105 1.2×	29 0.7×	18 0.4×	14 0.4×	5	183
Jan Quenzel Germany	8	154 1.7×	170 1.9×	75 1.7×	16 0.4×	29 0.9×	16	260
Oliver Wasenmüller Germany	9	189 2.0×	122 1.4×	53 1.2×	26 0.6×	29 0.9×	29	280

Countries citing papers authored by Stefan Milz

Since Specialization

Citations

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

Fields of papers citing papers by Stefan Milz

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Stefan Milz

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

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

1.

Milz, Stefan, et al.. (2023). The HAInich: A multidisciplinary vision data-set for a better understanding of the forest ecosystem. Scientific Data. 10(1). 168–168. 5 indexed citations

2.

Fischer, Kai, Martín Simón, Stefan Milz, & Patrick Mäder. (2022). StickyLocalization: Robust End-To-End Relocalization on Point Clouds using Graph Neural Networks. 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 307–316. 7 indexed citations

3.

Rabe, Martin, Stefan Milz, & Patrick Mäder. (2021). Development Methodologies for Safety Critical Machine Learning Applications in the Automotive Domain: A Survey. 129–141. 20 indexed citations

4.

Fischer, Kai, et al.. (2021). StickyPillars: Robust and Efficient Feature Matching on Point Clouds using Graph Neural Networks. 313–323. 52 indexed citations

5.

Simón, Martín, et al.. (2020). StickyPillars: Robust feature matching on point clouds using Graph Neural Networks. arXiv (Cornell University). 3 indexed citations

6.

Vaquero, V., Kai Fischer, Francesc Moreno-Noguer, Alberto Sanfeliu, & Stefan Milz. (2019). Improving map re-localization with deep 'movable' objects segmentation on 3D LiDAR point clouds. QRU Quaderns de Recerca en Urbanisme. 9 indexed citations

7.

Mohapatra, Sambit, et al.. (2019). Exploring Deep Spiking Neural Networks for Automated Driving Applications. 548–555. 1 indexed citations

8.

Mohapatra, Sambit, et al.. (2019). Exploring Deep Spiking Neural Networks for Automated Driving Applications. 548–555. 2 indexed citations

9.

Yogamani, Senthil, et al.. (2019). Capsule Neural Network based Height Classification using Low-Cost Automotive Ultrasonic Sensors. 661–666. 18 indexed citations

10.

Milz, Stefan, et al.. (2018). Visual SLAM for Automated Driving: Exploring the Applications of Deep Learning. 360–36010. 74 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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