Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Are we ready for autonomous driving? The KITTI vision benchmark suite
Countries citing papers authored by Andreas Geiger
Since
Specialization
Citations
This map shows the geographic impact of Andreas Geiger'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 Andreas Geiger with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andreas Geiger more than expected).
This network shows the impact of papers produced by Andreas Geiger. 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 Andreas Geiger. The network helps show where Andreas Geiger may publish in the future.
Co-authorship network of co-authors of Andreas Geiger
This figure shows the co-authorship network connecting the top 25 collaborators of Andreas Geiger.
A scholar is included among the top collaborators of Andreas Geiger 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 Andreas Geiger. Andreas Geiger is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Niemeyer, Michael, Jonathan T. Barron, Ben Mildenhall, et al.. (2022). RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 5470–5480.284 indexed citations breakdown →
Tosi, Fabio, et al.. (2021). SMD-Nets: Stereo Mixture Density Networks. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna).59 indexed citations
8.
Sauer, Axel & Andreas Geiger. (2021). Counterfactual Generative Networks. arXiv (Cornell University).4 indexed citations
Behl, Aseem, et al.. (2018). PointFlowNet: Learning Representations for 3D Scene Flow Estimation from Point Clouds.. arXiv (Cornell University).6 indexed citations
14.
Schöps, Thomas, Johannes L. Schönberger, Silvano Galliani, et al.. (2017). A Multi-view Stereo Benchmark with High-Resolution Images and Multi-camera Videos. 2538–2547.505 indexed citations breakdown →
Riegler, Gernot, Ali Osman Ulusoy, & Andreas Geiger. (2017). OctNet: Learning Deep 3D Representations at High Resolutions. 6620–6629.987 indexed citations breakdown →
17.
Geiger, Andreas, et al.. (2015). Object scene flow for autonomous vehicles. 3061–3070.1316 indexed citations breakdown →
18.
Geiger, Andreas, Christian Wojek, & Raquel Urtasun. (2011). Joint 3D Estimation of Objects and Scene Layout. Max Planck Institute for Plasma Physics. 24. 1467–1475.69 indexed citations
19.
Kitt, Bernd, Andreas Geiger, & Henning Lategahn. (2010). Visual odometry based on stereo image sequences with RANSAC-based outlier rejection scheme. 486–492.289 indexed citations breakdown →
20.
Li, Shutao, et al.. (2006). CAD/CAM integrated building prefabrication based on a product data model.1 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.