Zhihua Wang

2.4k total citations · 2 hit papers
9 papers, 1.5k citations indexed

About

Zhihua Wang is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics and Control and Systems Engineering. According to data from OpenAlex, Zhihua Wang has authored 9 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Computer Vision and Pattern Recognition, 3 papers in Computational Mechanics and 2 papers in Control and Systems Engineering. Recurrent topics in Zhihua Wang's work include 3D Shape Modeling and Analysis (3 papers), Wireless Power Transfer Systems (2 papers) and Human Pose and Action Recognition (2 papers). Zhihua Wang is often cited by papers focused on 3D Shape Modeling and Analysis (3 papers), Wireless Power Transfer Systems (2 papers) and Human Pose and Action Recognition (2 papers). Zhihua Wang collaborates with scholars based in United Kingdom, China and Malaysia. Zhihua Wang's co-authors include Andrew Markham, Niki Trigoni, Linhai Xie, Bo Yang, Stefano Rosa, Qingyong Hu, Yulan Guo, Sen Wang, Yishu Miao and Changhao Chen and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Industrial Electronics and IEEE Transactions on Neural Networks and Learning Systems.

In The Last Decade

Zhihua Wang

8 papers receiving 1.5k citations

Hit Papers

RandLA-Net: Efficient Semantic Segmentation of Large-Scal... 2020 2026 2022 2024 2020 2021 400 800 1.2k

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Zhihua Wang United Kingdom 6 803 802 797 546 272 9 1.5k
Linhai Xie China 10 804 1.0× 802 1.0× 792 1.0× 596 1.1× 325 1.2× 26 1.7k
Ruwen Schnabel Germany 10 924 1.2× 1.1k 1.4× 453 0.6× 789 1.4× 529 1.9× 14 1.9k
Andres Milioto Germany 12 562 0.7× 360 0.4× 354 0.4× 723 1.3× 547 2.0× 17 1.6k
Iro Armeni Switzerland 6 735 0.9× 1.1k 1.3× 751 0.9× 446 0.8× 246 0.9× 19 1.5k
Liangliang Nan Netherlands 22 885 1.1× 838 1.0× 375 0.5× 519 1.0× 344 1.3× 59 1.6k
Aleksey Golovinskiy United States 15 483 0.6× 580 0.7× 1.1k 1.4× 1.3k 2.4× 318 1.2× 16 2.0k
François Goulette France 15 486 0.6× 461 0.6× 265 0.3× 261 0.5× 332 1.2× 36 898
Ignacio Vizzo Germany 12 575 0.7× 457 0.6× 325 0.4× 722 1.3× 775 2.8× 18 1.4k
Jean‐Emmanuel Deschaud France 13 479 0.6× 430 0.5× 259 0.3× 240 0.4× 307 1.1× 31 843
Xian-Feng Han China 13 282 0.4× 329 0.4× 314 0.4× 536 1.0× 152 0.6× 28 1.1k

Countries citing papers authored by Zhihua Wang

Since Specialization
Citations

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

Fields of papers citing papers by Zhihua Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zhihua Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Zhihua Wang. A scholar is included among the top collaborators of Zhihua Wang 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 Zhihua Wang. Zhihua Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

9 of 9 papers shown
1.
Hu, Qingyong, Bo Yang, Linhai Xie, et al.. (2021). Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(11). 1–1. 157 indexed citations breakdown →
2.
Hu, Qingyong, Bo Yang, Linhai Xie, et al.. (2020). RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds. 11105–11114. 1288 indexed citations breakdown →
3.
Xie, Linhai, Yishu Miao, Sen Wang, et al.. (2020). Learning With Stochastic Guidance for Robot Navigation. IEEE Transactions on Neural Networks and Learning Systems. 32(1). 166–176. 33 indexed citations
4.
Wang, Zhihua & Andrew Markham. (2020). Wirelessly Powered Embedded Sensor Nodes for Internal Structural Health Monitoring. IEEE Transactions on Industrial Electronics. 72(9). 9780–9789. 24 indexed citations
5.
Wang, Zhihua, et al.. (2019). Maximal element with applications to Nash equilibrium problems in Hadamard manifolds. Optimization. 68(8). 1491–1520.
6.
Wang, Zhihua, Stefano Rosa, Bo Yang, et al.. (2018). 3D-PhysNet: Learning the Intuitive Physics of Non-Rigid Object Deformations. 4958–4964. 9 indexed citations
7.
Wang, Zhihua, Stefano Rosa, Linhai Xie, et al.. (2018). DEFO-NET: Learning Body Deformation Using Generative Adversarial Networks. 2440–2447. 7 indexed citations
8.
Wang, Zhihua, et al.. (2016). RePWR. 1–6. 1 indexed citations
9.
Wang, Zhihua, et al.. (2013). Using web crawler technology to support design-related web information collection in idea generation. Spiral (Imperial College London). 229–238. 2 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.

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