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.
Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification
2018343 citationsJulie Chang, Vincent Sitzmann et al.Scientific Reportsprofile →
Saliency in VR: How Do People Explore Virtual Environments?
2018313 citationsVincent Sitzmann, Ana Serrano et al.profile →
Neural Fields in Visual Computing and Beyond
2022275 citationsYiheng Xie, Towaki Takikawa et al.Computer Graphics Forumprofile →
Advances in Neural Rendering
2022203 citationsAyush Tewari, Justus Thies et al.Computer Graphics Forumprofile →
PixelSplat: 3D Gaussian Splats from Image Pairs for Scalable Generalizable 3D Reconstruction
202457 citationsAndrea Tagliasacchi, Vincent Sitzmann et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Vincent Sitzmann
Since
Specialization
Citations
This map shows the geographic impact of Vincent Sitzmann'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 Vincent Sitzmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vincent Sitzmann more than expected).
Fields of papers citing papers by Vincent Sitzmann
This network shows the impact of papers produced by Vincent Sitzmann. 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 Vincent Sitzmann. The network helps show where Vincent Sitzmann may publish in the future.
Co-authorship network of co-authors of Vincent Sitzmann
This figure shows the co-authorship network connecting the top 25 collaborators of Vincent Sitzmann.
A scholar is included among the top collaborators of Vincent Sitzmann 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 Vincent Sitzmann. Vincent Sitzmann is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Tagliasacchi, Andrea, et al.. (2024). PixelSplat: 3D Gaussian Splats from Image Pairs for Scalable Generalizable 3D Reconstruction. 19457–19467.57 indexed citations breakdown →
Ambruş, Rareş, Dennis Park, Wadim Kehl, et al.. (2021). Single-Shot Scene Reconstruction.5 indexed citations
16.
Du, Yilun, Katherine M. Collins, Josh Tenenbaum, & Vincent Sitzmann. (2021). Learning Signal-Agnostic Implicit Manifolds. Neural Information Processing Systems. 34.1 indexed citations
17.
Sitzmann, Vincent, Eric R. Chan, Richard Tucker, Noah Snavely, & Gordon Wetzstein. (2020). MetaSDF: Meta-Learning Signed Distance Functions. arXiv (Cornell University). 33. 10136–10147.9 indexed citations
18.
Chang, Julie, Vincent Sitzmann, Xiong Dun, Wolfgang Heidrich, & Gordon Wetzstein. (2018). Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification. Scientific Reports. 8(1). 12324–12324.343 indexed citations breakdown →
Sitzmann, Vincent, Ana Serrano, Amy Pavel, et al.. (2016). Saliency in VR: How do people explore virtual environments?. Zaguan (Universidad de Zaragoza).15 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.