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.
Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving
2019701 citationsYan Wang, Wei‐Lun Chao et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Mark Campbell'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 Mark Campbell with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Campbell more than expected).
This network shows the impact of papers produced by Mark Campbell. 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 Mark Campbell. The network helps show where Mark Campbell may publish in the future.
Co-authorship network of co-authors of Mark Campbell
This figure shows the co-authorship network connecting the top 25 collaborators of Mark Campbell.
A scholar is included among the top collaborators of Mark Campbell 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 Mark Campbell. Mark Campbell is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Banfi, Jacopo, et al.. (2020). Planning Paths Through Unknown Space by Imagining What Lies Therein. 905–914.1 indexed citations
3.
Garg, Divyansh, Yan Wang, Bharath Hariharan, et al.. (2020). Wasserstein Distances for Stereo Disparity Estimation. Neural Information Processing Systems. 33. 22517–22529.1 indexed citations
4.
Wang, Yan, Wei‐Lun Chao, Divyansh Garg, et al.. (2019). Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. 8437–8445.701 indexed citations breakdown →
Campbell, Mark, et al.. (2017). Priority-Based Tracking of Extended Objects. 12(1).2 indexed citations
8.
Ahmed, Nisar, et al.. (2014). A Look at Probabilistic Gaussian Process, Bayes Net, and Classifier Models for Prediction and Verification of Human Supervisory Performance. Journal of International Crisis and Risk Communication Research.1 indexed citations
Campbell, Mark, et al.. (2009). Distributed terrain estimation using a mixture-model based algorithm. International Conference on Information Fusion. 960–967.10 indexed citations
Kulkarni, Jayant & Mark Campbell. (2004). An Approach to Magnetic Torque Attitude Control of Satellites via 'H∞' Control for LTV Systems.11 indexed citations
15.
D’Andrea, Raffaello, et al.. (2003). RoboFlag – A Framework for Exploring Control , Planning , and Human Interface Issues Related to Coordinating Multiple Robots in a Realtime Dynamic Environment.2 indexed citations
Campbell, Mark, et al.. (2000). Comparison of Multiple Agent-Based Organizations for Satellite Constellations (TechSat21). The Florida AI Research Society. 250–259.3 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.