Tai Wang

622 citations
9 papers · 240 indexed · h-index 6
Topics
Advanced Neural Network Applications (5 papers)Robotics and Sensor-Based Localization (4 papers)3D Surveying and Cultural Heritage (2 papers)
Journals
Angewandte Chemie International EditionIEEE Transactions on Pattern Analysis and Machine Intelligence2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

In The Last Decade

Tai Wang

7 papers receiving 234 citations

Peers

Tai Wang
Comparison fields: 5 of 38
  • Computer Vision and Pattern Recognition 172
  • Aerospace Engineering 88
  • Computational Mechanics 52
  • Environmental Engineering 49
  • Geology 44
Replace Lue Fan with:
Lue Fan China
Anna Vorontsova United States
Min-Jian Zhao China
Jean Lahoud United Arab Emirates
Armin Mustafa United Kingdom
Liang Xie China
Biao Gao China
Jiantao Gao China
Hazem Rashed Egypt
Tai Wang relative to Lue Fan China Lue Fan's profile →
Citations per field
00.5×1.5×
Lue Fan · 1×
Citations per year

Countries citing papers authored by Tai Wang

Since Specialization
Citations

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

Fields of papers citing papers by Tai Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tai Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Tai Wang. A scholar is included among the top collaborators of Tai 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 Tai Wang. Tai 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
#WorkIndexed citations
1 0
2 29
3 0
4 6
5 87
6 17
7
Balanced Chamfer Distance as a Comprehensive Metric for Point Cloud Completion
21
8 78
9 2

About Tai Wang

Tai Wang is a scholar working on Geology, Computer Vision and Pattern Recognition and Aerospace Engineering, having authored 9 papers that have together received 240 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (5 papers), Robotics and Sensor-Based Localization (4 papers) and 3D Surveying and Cultural Heritage (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (172 citations), Geology (44 citations) and Computer Graphics and Computer-Aided Design (16 citations). Tai Wang has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Hongsheng Li, Xinge Zhu, Yuexin Ma, Yu Qiao, Dahua Lin, Ruigang Yang, Z. J. Guo, Peng Gao, Han Qiu and Ziteng Cui. Their work appears in journals such as Angewandte Chemie International Edition, IEEE Transactions on Pattern Analysis and Machine Intelligence and 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).

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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