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3D Shape Modeling and Analysis

3D Shape Modeling and Analysis is a research topic within Computational Mechanics. Science Explorer counts 27k research works in it since 1951. 24.3% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the analysis, reconstruction, and representation of three-dimensional shapes using deep learning techniques, point clouds, and mesh processing. It covers topics such as 3D classification, segmentation, object recognition, shape reconstruction from single images, surface parameterization, mesh deformation, and texture mapping.

  • Deep Learning
  • Point Clouds
  • 3D Reconstruction
  • Mesh Segmentation
  • Shape Representation
  • Neural Radiance Fields
  • Surface Parameterization
  • Mesh Deformation
  • Texture Mapping
  • Point Set Surfaces
Research works
27k
fractional, since 1951
In the world top 10%
6.5k
per year above
Top-10% rate
24.3%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+27%
the tick is no change

Which countries lead 3D Shape Modeling and Analysis research?

By volume, China and the United States publish the most (2.7k and 815 works in 2022–2025).

By volume, 2022–2025

  1. 1 China 2.7k works
  2. 2 United States 815 works
  3. 3 Germany 294 works
  4. 4 Japan 220 works
  5. 5 United Kingdom 205 works
  6. 6 South Korea 194 works
  7. 7 France 190 works
  8. 8 India 167 works
  9. 9 Hong Kong 142 works
  10. 10 Canada 140 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 53.7%United States: 15.9%Germany: 5.8%Japan: 4.3%6 others listed: 20.3%54%largest
China2,745 · 53.7%United States815 · 15.9%Germany294 · 5.8%Japan220 · 4.3%6 others listed1,039 · 20.3%

Shares of the rows listed above, not of the whole node.

Which institutions lead 3D Shape Modeling and Analysis research?

By volume in 2022–2025, Tsinghua University publishes the most 3D Shape Modeling and Analysis research, followed by Shanghai Jiao Tong University and Zhejiang University.

Who are the leading researchers in 3D Shape Modeling and Analysis?

The most-cited researchers publishing on 3D Shape Modeling and Analysis include Xiaogang Wang, Jitendra Malik and James Hays.

  1. 1 Xiaogang Wang Russia 13k citations
  2. 2 Jitendra Malik United States 12k citations
  3. 3 James Hays United States 11k citations
  4. 4 Deva Ramanan United States 11k citations
  5. 5 Sebastian Thrun United States 10k citations
  6. 6 Wei Liu China 9.7k citations
  7. 7 Hao Su United States 9.2k citations
  8. 8 Luc Van Gool Switzerland 8.5k citations
  9. 9 Alan Yuille United States 8.4k citations
  10. 10 Stanley Osher United States 8k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is 3D Shape Modeling and Analysis research done?

The largest centres of 3D Shape Modeling and Analysis research in 2022–2025 are Beijing (China), Shanghai (China), Hangzhou (China) and Wuhan (China). Among places with at least 20 works in it, it is an unusually large share of all research in San Jose and Mountain View.

Largest cities, 2022–2025

  1. 1 Beijing China 601 works
  2. 2 Shanghai China 268 works
  3. 3 Hangzhou China 177 works
  4. 4 Wuhan China 156 works
  5. 5 Nanjing China 150 works
  6. 6 Xi'an China 146 works
  7. 7 Shenzhen China 127 works
  8. 8 Hong Kong China 118 works
  9. 9 Seoul South Korea 99 works
  10. 10 Guangzhou China 96 works

Where it is the local speciality

  1. San JoseUS · 26.1 works13×
  2. Mountain ViewUS · 23.1 works6.8×
← less than its size predictsmore →

Location quotient: how much more of its research is in 3D Shape Modeling and Analysis than the world average.

See 3D Shape Modeling and Analysis on the map

Where is the best place to study 3D Shape Modeling and Analysis?

Among universities, judged by research, Tsinghua University, ShanghaiTech University and Nanyang Technological University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%60%mean 42.84%fractional works in this node (log) →share in the world top 10% →Tsinghua University: 98, 42.1%ShanghaiTech University: 16, 52.5%Nanyang Technological University: 40, 44.7%Carnegie Mellon University: 29, 41.5%ETH Zurich: 29, 49.8%Hong Kong University of Science and Technology: 21, 43.7%Peking University: 64, 42.8%Chinese University of Hong Kong, Shenzhen: 12, 36.3%University of Hong Kong: 30, 47.2%Shenzhen University: 26, 27.8%ShanghaiTech Univers…Nanyang Technologica…Tsinghua UniversityCarnegie Mellon Univ…
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Tsinghua UniversityChina 59.842.1%8.2×98 -14.4%
2 ShanghaiTech UniversityChina 58.452.5%14.0×16
3 Nanyang Technological UniversitySingapore 58.144.7%8.3×40 -43.7%
4 Carnegie Mellon UniversityUnited States 57.141.5%11.7×29 +10.8%
5 ETH ZurichSwitzerland 55.849.8%7.1×29 +7.1%
6 Hong Kong University of Science and TechnologyHong Kong 55.343.7%8.8×21 -35.2%
7 Peking UniversityChina 55.142.8%6.9×64 +11.7%
8 Chinese University of Hong Kong, ShenzhenChina 53.136.3%10.3×12
9 University of Hong KongHong Kong 52.047.2%5.8×30 -20.1%
10 Shenzhen UniversityChina 51.027.8%5.8×26 +276.2%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is 3D Shape Modeling and Analysis research growing?

Output in 2018–2022 was 27% higher than in 2013–2017, peaking in 2025.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.