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.
A benchmark for the evaluation of RGB-D SLAM systems
Countries citing papers authored by Daniel Cremers
Since
Specialization
Citations
This map shows the geographic impact of Daniel Cremers'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 Daniel Cremers with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Cremers more than expected).
This network shows the impact of papers produced by Daniel Cremers. 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 Daniel Cremers. The network helps show where Daniel Cremers may publish in the future.
Co-authorship network of co-authors of Daniel Cremers
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Cremers.
A scholar is included among the top collaborators of Daniel Cremers 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 Daniel Cremers. Daniel Cremers is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Nießner, Matthias, et al.. (2019). Linear Inequality Constraints for Neural Network Activations.. arXiv (Cornell University).2 indexed citations
8.
Stumberg, Lukas von, et al.. (2019). GN-Net: The Gauss-Newton Loss for Deep Direct SLAM.. arXiv (Cornell University).4 indexed citations
9.
Henschel, Roberto, Laura Leal-Taixé, Daniel Cremers, & Bodo Rosenhahn. (2017). A Novel Multi-Detector Fusion Framework for Multi-Object Tracking. arXiv (Cornell University).9 indexed citations
10.
Henschel, Roberto, Laura Leal-Taixé, Daniel Cremers, & Bodo Rosenhahn. (2017). Improvements to Frank-Wolfe optimization for multi-detector multi-object tracking.. arXiv (Cornell University).13 indexed citations
11.
Yang, Nan, Rui Wang, & Daniel Cremers. (2017). Feature-based or Direct: An Evaluation of Monocular Visual Odometry.. arXiv (Cornell University).9 indexed citations
Hazırbaş, Caner, et al.. (2016). Image-based Localization with Spatial LSTMs.. arXiv (Cornell University).28 indexed citations
14.
Geiping, Jonas, et al.. (2016). Multiframe Motion Coupling via Infimal Convolution Regularization for Video Super Resolution.. arXiv (Cornell University).1 indexed citations
15.
Cremers, Daniel, et al.. (2014). LSD-SLAM:大規模有向単眼SLAM. Lecture notes in computer science. 8690. 834–849.2 indexed citations
16.
Wedel, Andreas, et al.. (2009). TV-L 1 オプティカルフローのための改良アルゴリズム. Lecture notes in computer science. 5604. 23–45.1 indexed citations
17.
Sellent, Anita, Martin Eisemann, Bastian Goldlücke, et al.. (2009). Variational Optical Flow from Alternate Exposure Images. Vision Modeling and Visualization. 135–143.3 indexed citations
18.
Cremers, Daniel, Yuri Boykov, A. Blake, & Frank Schmidt. (2009). Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition.2 indexed citations
19.
Cremers, Daniel, Yuri Boykov, A. Blake, & Frank Schmidt. (2009). Energy Minimization Methods in Computer Vision and Pattern Recognition: 7th International Conference, EMMCVPR 2009, Bonn, Germany, August 24-27, 2009, ... Vision, Pattern Recognition, and Graphics. Springer eBooks.1 indexed citations
20.
Pock, Thomas, et al.. (2009). Advanced Data Terms for Variational Optic Flow Estimation. Vision Modeling and Visualization. 155–164.19 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.