Or Litany

4.0k citations
33 papers · 1.3k indexed · 2 hit papers · h-index 17

Or Litany

31 papers receiving 1.2k citations

Hit Papers

Mask3D: Mask Transformer for 3D Semantic Instance Segment...94202220262023202450100150200250

Peers

Or Litany
Comparison fields: 5 of 97
  • Computer Graphics and Computer-Aided Design 315
  • Computer Vision and Pattern Recognition 755
  • Geology 173
  • Computational Mechanics 505
  • Automotive Engineering 95
Replace Thomas Leimkühler with:
Thomas Leimkühler Germany
Bernhard Kerbl Austria
Georgios Kopanas France
Lingjie Liu United States
Kyle Genova United States
Towaki Takikawa Canada
Klaus Hildebrandt Netherlands
George Vogiatzis United Kingdom
Dor Verbin United States
Alex Evans United Kingdom
Or Litany relative to Thomas Leimkühler Germany Thomas Leimkühler's profile →
Citations per field
00.5×10×15×22.4×
Thomas Leimkühler · 1×
Citations per year

Countries citing papers authored by Or Litany

Since Specialization
Citations

This map shows the geographic impact of Or Litany'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 Or Litany with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Or Litany more than expected).

Fields of papers citing papers by Or Litany

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Or Litany. 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 Or Litany. The network helps show where Or Litany may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Or Litany, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Or Litany Line = papers co-authored together Or Litany links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 202410
3 20242
4 20242
5 20248
6 202330
7
Mask3D: Mask Transformer for 3D Semantic Instance Segmentationbreakdown →
202394
8 20235
9 202338
10 20236
11
Neural Fields in Visual Computing and Beyondbreakdown →
2022275
12 202235
13 202244
14 202275
15
On Learning Sets of Symmetric Elements
20208
16 201979
17 20174
18 201716
19 201755
20
A Picture is worth a Billion Bits: Real-Time Image Reconstruction from Dense Binary Pixels
20152

About Or Litany

Or Litany is a scholar working on Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition, Geology, Computational Mechanics and Automotive Engineering, having authored 33 papers that have together received 1.3k indexed citations. Recurring topics across this work include 3D Shape Modeling and Analysis (14 papers), Computer Graphics and Visualization Techniques (8 papers), Advanced Vision and Imaging (8 papers), Advanced Neural Network Applications (6 papers), Robotics and Sensor-Based Localization (6 papers), 3D Surveying and Cultural Heritage (5 papers), Human Pose and Action Recognition (4 papers) and Advanced Numerical Analysis Techniques (4 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (315 citations), Computer Vision and Pattern Recognition (755 citations), Geology (173 citations), Computational Mechanics (505 citations) and Automotive Engineering (95 citations). Or Litany has collaborated with scholars based in United States, Israel and Canada. Frequent co-authors include Leonidas Guibas, Alex Bronstein, Sanja Fidler, Emanuele Rodolà, Srinath Sridhar, Žan Gojčič, Federico Tombari, Yiheng Xie, Numair Khan and Shunsuke Saito. Their work appears in journals such as Computer Graphics Forum, SPIRE - Sciences Po Institutional REpository, arXiv (Cornell University), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and International Conference on Machine Learning.

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.

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