Gary K.L. Tam

1.3k total citations · 1 hit paper
29 papers, 834 citations indexed

About

Gary K.L. Tam is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics and Computer Graphics and Computer-Aided Design. According to data from OpenAlex, Gary K.L. Tam has authored 29 papers receiving a total of 834 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Computer Vision and Pattern Recognition, 14 papers in Computational Mechanics and 9 papers in Computer Graphics and Computer-Aided Design. Recurrent topics in Gary K.L. Tam's work include 3D Shape Modeling and Analysis (14 papers), Computer Graphics and Visualization Techniques (9 papers) and Visual Attention and Saliency Detection (5 papers). Gary K.L. Tam is often cited by papers focused on 3D Shape Modeling and Analysis (14 papers), Computer Graphics and Visualization Techniques (9 papers) and Visual Attention and Saliency Detection (5 papers). Gary K.L. Tam collaborates with scholars based in United Kingdom, Hong Kong and China. Gary K.L. Tam's co-authors include Paul L. Rosin, Yu‐Kun Lai, David Marshall, Zhi‐Quan Cheng, R. R. Martin, Frank C. Langbein, Yonghuai Liu, Xianfang Sun, Rynson W. H. Lau and Xianghua Xie and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, ACM Transactions on Graphics and Pattern Recognition.

In The Last Decade

Gary K.L. Tam

29 papers receiving 796 citations

Hit Papers

align trajectories

What are hit papers?

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.

Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid

2012 452 citations Gary K.L. Tam, Zhi‐Quan Cheng et al. IEEE Transactions on Visualization and Computer Graphics profile →

Peers

Gary K.L. Tam

CVPR

CM

AE

GEOLO

CGCD

Sen Wang China

Zhi‐Quan Cheng China

Tianjia Shao China

Thorsten Thormählen Germany

Lu Sheng China

R. R. Martin United Kingdom

David Novotný United Kingdom

Hang Chu United States

Ray Jarvis Australia

Rohit Girdhar United States

Sen Wang China

Gary K.L. Tam

872 ×1.5

CVPR

338 ×1.2

CM

173 ×0.7

AE

66 ×0.3

GEOLO

116 ×1.0

CGCD

Citations per year, relative to Gary K.L. Tam Gary K.L. Tam (= 1×) peers Sen Wang

Countries citing papers authored by Gary K.L. Tam

Since Specialization

Citations

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

Fields of papers citing papers by Gary K.L. Tam

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gary K.L. Tam. 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 Gary K.L. Tam. The network helps show where Gary K.L. Tam may publish in the future.

Co-authorship network of co-authors of Gary K.L. Tam

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Tam, Gary K.L., et al.. (2025). Survey on 3D Reconstruction Techniques: Large-Scale Urban City Reconstruction and Requirements. IEEE Transactions on Visualization and Computer Graphics. 31(10). 9343–9367. 2 indexed citations

2.

Hill, Lyndon M., et al.. (2025). Denoising-While-Completing Network (DWCNet): Robust point cloud completion under corruption. Computers & Graphics. 132. 104401–104401. 1 indexed citations

3.

Xu, Ke, et al.. (2024). Effective Video Mirror Detection with Inconsistent Motion Cues. 17244–17252. 4 indexed citations

4.

Wang, Guo‐Liang, et al.. (2023). An end-to-end dynamic point cloud geometry compression in latent space. Displays. 80. 102528–102528. 1 indexed citations

5.

Tam, Gary K.L., et al.. (2023). C2SPoint: A classification-to-saliency network for point cloud saliency detection. Computers & Graphics. 115. 274–284. 2 indexed citations

6.

Hill, Lyndon M., et al.. (2023). Point Cloud Completion: A Survey. IEEE Transactions on Visualization and Computer Graphics. 30(10). 6880–6899. 10 indexed citations

7.

Jiao, Jianbo, et al.. (2023). Inferring Attention Shifts for Salient Instance Ranking. International Journal of Computer Vision. 132(3). 964–986. 1 indexed citations

8.

Jiao, Jianbo, et al.. (2022). Scene context-aware salient object detection. Oxford University Research Archive (ORA) (University of Oxford). 51 indexed citations

9.

Jiao, Jianbo, et al.. (2021). Scene Context-Aware Salient Object Detection. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 4136–4146. 1 indexed citations

10.

Jiao, Jianbo, et al.. (2020). Inferring Attention Shift Ranks of Objects for Image Saliency. Cronfa (Swansea University). 12130–12140. 25 indexed citations

11.

George, David Solomon, Xianghua Xie, & Gary K.L. Tam. (2018). 3D mesh segmentation via multi-branch 1D convolutional neural networks. Graphical Models. 96. 1–10. 30 indexed citations

12.

Tam, Gary K.L., et al.. (2016). An Analysis of Machine- and Human-Analytics in Classification. IEEE Transactions on Visualization and Computer Graphics. 23(1). 71–80. 52 indexed citations

13.

Tam, Gary K.L., et al.. (2015). Computing 3D Mesh Correspondence for Aortic Root Shape Modelling. 92–97. 1 indexed citations

14.

Tam, Gary K.L., Ralph R. Martin, Paul L. Rosin, & Yu‐Kun Lai. (2014). An Efficient Approach to Correspondences between Multiple Non‐Rigid Parts. Computer Graphics Forum. 33(5). 137–146. 3 indexed citations

15.

Tam, Gary K.L., Ralph R. Martin, Paul L. Rosin, & Yu‐Kun Lai. (2014). Diffusion pruning for rapidly and robustly selecting global correspondences using local isometry. ACM Transactions on Graphics. 33(1). 1–17. 41 indexed citations

16.

Fang, Hui, Gary K.L. Tam, Rita Borgo, et al.. (2012). Visualizing Natural Image Statistics. IEEE Transactions on Visualization and Computer Graphics. 19(7). 1228–1241. 8 indexed citations

17.

Tam, Gary K.L. & Rynson W. H. Lau. (2012). Embedding Retrieval of Articulated Geometry Models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 34(11). 2134–2146. 6 indexed citations

18.

Tam, Gary K.L., Zhi‐Quan Cheng, Yu‐Kun Lai, et al.. (2012). Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid. IEEE Transactions on Visualization and Computer Graphics. 19(7). 1199–1217. 452 indexed citations breakdown →

19.

Tam, Gary K.L. & Rynson W. H. Lau. (2007). Deformable Model Retrieval Based on Topological and Geometric Signatures. IEEE Transactions on Visualization and Computer Graphics. 13(3). 470–482. 31 indexed citations

20.

Tam, Gary K.L., Rynson W. H. Lau, & Chong‐Wah Ngo. (2004). Deformable object model matching by topological and geometric similarity. 335–342. 1 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.

Explore authors with similar magnitude of impact