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.
Countries citing papers authored by Ryan M. Eustice
Since
Specialization
Citations
This map shows the geographic impact of Ryan M. Eustice'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 Ryan M. Eustice with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ryan M. Eustice more than expected).
This network shows the impact of papers produced by Ryan M. Eustice. 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 Ryan M. Eustice. The network helps show where Ryan M. Eustice may publish in the future.
Co-authorship network of co-authors of Ryan M. Eustice
This figure shows the co-authorship network connecting the top 25 collaborators of Ryan M. Eustice.
A scholar is included among the top collaborators of Ryan M. Eustice 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 Ryan M. Eustice. Ryan M. Eustice is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Huang, Jiunn-Kai, Maani Ghaffari, Ross Hartley, et al.. (2019). LiDARTag: A Real-Time Fiducial Tag using Point Clouds. arXiv (Cornell University).3 indexed citations
Gan, Lu, et al.. (2018). Semantic Iterative Closest Point through Expectation-Maximization.. British Machine Vision Conference. 280.15 indexed citations
5.
Eustice, Ryan M., et al.. (2018). Feature Learning for Scene Flow Estimation from LIDAR. 283–292.7 indexed citations
Wolcott, Ryan W. & Ryan M. Eustice. (2014). Visual localization within LIDAR maps for automated urban driving. 176–183.276 indexed citations breakdown →
Pandey, Gaurav, James R. McBride, & Ryan M. Eustice. (2011). Ford Campus vision and lidar data set. The International Journal of Robotics Research. 30(13). 1543–1552.237 indexed citations
Kim, Ayoung & Ryan M. Eustice. (2009). Pose-graph visual SLAM with geometric model selection for autonomous underwater ship hull inspection.1 indexed citations
15.
Kim, Ayoung & Ryan M. Eustice. (2009). Toward AUV Survey Design for Optimal Coverage and Localization Using the Cramer Rao Lower Bound. Deep Blue (University of Michigan).
16.
Singh, Hanumant, et al.. (2005). Advances in high-resolution imaging from underwater vehicles.3 indexed citations
Hill, J. C., N. W. Driscoll, Jeffrey K. Weissel, et al.. (2004). A Potential Link between Fluid Expulsion and Slope Stability: Geochemical Anomalies Measured in the Gas Blowouts along the U.S. Atlantic Margin Provide New Constraints on their Formation. AGUFM. 2004.1 indexed citations
19.
Cormier, Marie‐Hélène, Jeffrey K. Weissel, N. W. Driscoll, et al.. (2004). A Detailed Near-bottom Survey of Large Gas Blowout Structures Along the US Atlantic Shelf Break Using the Autonomous Underwater Vehicle (AUV) SeaBED. AGU Fall Meeting Abstracts. 2004.4 indexed citations
20.
Pizarro, Oscar, Ryan M. Eustice, & Hanumant Singh. (2003). Relative Pose Estimation for Instrumented, Calibrated Imaging Platforms. 601–612.49 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.