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.
KinectFusion: Real-time dense surface mapping and tracking
20112.6k citationsAndrew Fitzgibbon, Shahram Izadi et al.profile →
KinectFusion
20111.4k citationsShahram Izadi, David Kim et al.profile →
Real-time 3D reconstruction at scale using voxel hashing
2013615 citationsMatthias Nießner, Shahram Izadi et al.profile →
Scene Coordinate Regression Forests for Camera Relocalization in RGB-D Images
2013546 citationsJamie Shotton, Ben Glocker et al.profile →
BundleFusion
2017394 citationsMatthias Nießner, Shahram Izadi et al.profile →
Fusion4D
2016317 citationsPhilip Davidson, Sean Fanello et al.profile →
Accurate, Robust, and Flexible Real-time Hand Tracking
2015285 citationsCem Keskin, Jonathan M. Taylor et al.profile →
Real-time non-rigid reconstruction using an RGB-D camera
2014262 citationsMatthias Nießner, Shahram Izadi et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Shahram Izadi'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 Shahram Izadi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shahram Izadi more than expected).
This network shows the impact of papers produced by Shahram Izadi. 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 Shahram Izadi. The network helps show where Shahram Izadi may publish in the future.
Co-authorship network of co-authors of Shahram Izadi
This figure shows the co-authorship network connecting the top 25 collaborators of Shahram Izadi.
A scholar is included among the top collaborators of Shahram Izadi 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 Shahram Izadi. Shahram Izadi is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Meka, Abhimitra, Rohit Pandey, Christian Häne, et al.. (2020). Deep Relightable Textures Volumetric Performance Capture with Neural Rendering. MPG.PuRe (Max Planck Society).36 indexed citations
Mikšík, Ondřej, Vibhav Vineet, Matthias Nießner, et al.. (2015). The Semantic Paintbrush. Human Factors in Computing Systems.2 indexed citations
4.
Valentin, Julien, Vibhav Vineet, Ming‐Ming Cheng, et al.. (2015). SemanticPaint: Interactive 3D Labeling and Learning at your Fingertips. arXiv (Cornell University). 34(5). 154.57 indexed citations
Izadi, Shahram, Aaron Quigley, Ivan Poupyrev, & Takeo Igarashi. (2013). Proceedings of the adjunct publication of the 26th annual ACM symposium on User interface software and technology.12 indexed citations
8.
Izadi, Shahram, et al.. (2013). Evaluation of the relationship between competitive advantage and export performance (Case study: Iranian firms exporting biotech products). European Journal of Experimental Biology. 3(1).5 indexed citations
9.
Shotton, Jamie, Ben Glocker, Christopher Zach, et al.. (2013). Scene Coordinate Regression Forests for Camera Relocalization in RGB-D Images. 2930–2937.546 indexed citations breakdown →
10.
Hirsch, Matthew, Shahram Izadi, Henry Holtzman, & Ramesh Raskar. (2013). 8D. DSpace@MIT (Massachusetts Institute of Technology). 2209–2212.7 indexed citations
11.
Meister, Stephan, Shahram Izadi, Pushmeet Kohli, et al.. (2012). When Can We Use KinectFusion for Ground Truth Acquisition.53 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.