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.
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
20201.3k citationsQingyong Hu, Bo Yang et al.profile →
Visual SLAM and Structure from Motion in Dynamic Environments
2018279 citationsMuhamad Risqi U. Saputra, Andrew Markham et al.profile →
SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration
2021231 citationsSheng Ao, Qingyong Hu et al.profile →
Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling
2021157 citationsQingyong Hu, Bo Yang et al.IEEE Transactions on Pattern Analysis and Machine Intelligenceprofile →
Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations
2023152 citationsBen Moseley, Andrew Markham et al.Advances in Computational Mathematicsprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Andrew Markham
Since
Specialization
Citations
This map shows the geographic impact of Andrew Markham'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 Andrew Markham with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrew Markham more than expected).
This network shows the impact of papers produced by Andrew Markham. 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 Andrew Markham. The network helps show where Andrew Markham may publish in the future.
Co-authorship network of co-authors of Andrew Markham
This figure shows the co-authorship network connecting the top 25 collaborators of Andrew Markham.
A scholar is included among the top collaborators of Andrew Markham 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 Andrew Markham. Andrew Markham is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Xie, Linhai, Sen Wang, Andrew Markham, & Niki Trigoni. (2017). Towards Monocular Vision based Obstacle Avoidance through Deep Reinforcement Learning. Oxford University Research Archive (ORA) (University of Oxford).9 indexed citations
Abrudan, Traian E., Andrew Markham, Niki Trigoni, & Zhuoling Xiao. (2015). Distortion Rejecting Magneto−inductive 3−D Localization (MagLoc). IEEE Journal on Selected Areas in Communications.5 indexed citations
18.
Zhao, Xiaojie, Zhuoling Xiao, Andrew Markham, Niki Trigoni, & Yong Ren. (2014). Does BTLE measure up against WiFi? A comparison of indoor location performance. Oxford University Research Archive (ORA) (University of Oxford). 1–6.106 indexed citations
19.
Markham, Andrew & Niki Trigoni. (2014). Climate and the Individual: Inter−Annual Variation in the Autumnal Activity of the European Badger. PLoS ONE.2 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.