Chen Kong

831 total citations · 1 hit paper
9 papers, 379 citations indexed

About

Chen Kong is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Computational Mechanics. According to data from OpenAlex, Chen Kong has authored 9 papers receiving a total of 379 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Computer Vision and Pattern Recognition, 4 papers in Aerospace Engineering and 3 papers in Computational Mechanics. Recurrent topics in Chen Kong's work include Advanced Vision and Imaging (6 papers), Robotics and Sensor-Based Localization (4 papers) and 3D Shape Modeling and Analysis (3 papers). Chen Kong is often cited by papers focused on Advanced Vision and Imaging (6 papers), Robotics and Sensor-Based Localization (4 papers) and 3D Shape Modeling and Analysis (3 papers). Chen Kong collaborates with scholars based in United States, Australia and China. Chen Kong's co-authors include Simon Lucey, Chen-Hsuan Lin, Chaoyang Wang, Sanja Fidler, Dahua Lin, Raquel Urtasun, Rui Zhu, Hamed Kiani Galoogahi, Jhony Kaesemodel Pontes and Clinton Fookes and has published in prestigious journals such as arXiv (Cornell University) and Proceedings of the AAAI Conference on Artificial Intelligence.

In The Last Decade

Chen Kong

9 papers receiving 369 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.

Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction

2018 224 citations Chen-Hsuan Lin, Chen Kong et al. Proceedings of the AAAI Conference on Artificial Intelligence profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Chen Kong United States	8	285	231	131	91	71	9	379
Patrick Degener Germany	8	201 0.7×	214 0.9×	181 1.4×	72 0.8×	40 0.6×	12	348
Jiapeng Tang China	8	214 0.8×	217 0.9×	168 1.3×	61 0.7×	22 0.3×	17	350
Stefan Leopoldseder Austria	7	168 0.6×	329 1.4×	159 1.2×	75 0.8×	80 1.1×	8	456
Xinhan Di China	3	176 0.6×	249 1.1×	84 0.6×	201 2.2×	61 0.9×	7	390
Tristan Laidlow United Kingdom	6	280 1.0×	75 0.3×	69 0.5×	77 0.8×	146 2.1×	8	370
JunYoung Gwak United States	6	184 0.6×	107 0.5×	56 0.4×	49 0.5×	48 0.7×	6	239
Songfang Han United States	8	308 1.1×	114 0.5×	77 0.6×	117 1.3×	73 1.0×	12	412
Lingni Ma Netherlands	10	284 1.0×	88 0.4×	43 0.3×	133 1.5×	194 2.7×	17	386
Haithem Turki United States	6	204 0.7×	67 0.3×	120 0.9×	39 0.4×	56 0.8×	7	294
Alireza Javaheri Portugal	7	168 0.6×	158 0.7×	107 0.8×	75 0.8×	22 0.3×	8	272

Countries citing papers authored by Chen Kong

Since Specialization

Citations

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

Fields of papers citing papers by Chen Kong

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chen Kong

This figure shows the co-authorship network connecting the top 25 collaborators of Chen Kong. A scholar is included among the top collaborators of Chen Kong 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 Chen Kong. Chen Kong 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:

9 of 9 papers shown

1.

Kong, Chen, et al.. (2020). A Video Based Method of Occlusion Vehicle Completion. 1368–1372. 2 indexed citations

2.

Wang, Chaoyang, Chen Kong, & Simon Lucey. (2019). Distill Knowledge From NRSfM for Weakly Supervised 3D Pose Learning. 743–752. 27 indexed citations

3.

Kong, Chen & Simon Lucey. (2019). Deep Non-Rigid Structure From Motion. 1558–1567. 33 indexed citations

4.

Lin, Chen-Hsuan, Chen Kong, & Simon Lucey. (2018). Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 224 indexed citations breakdown →

5.

Kong, Chen, Chen-Hsuan Lin, & Simon Lucey. (2017). Using Locally Corresponding CAD Models for Dense 3D Reconstructions from a Single Image. 5603–5611. 29 indexed citations

6.

Pontes, Jhony Kaesemodel, Chen Kong, Anders Eriksson, et al.. (2017). Compact Model Representation for 3D Reconstruction. arXiv (Cornell University). 88–96. 13 indexed citations

7.

Kong, Chen & Simon Lucey. (2016). Prior-Less Compressible Structure from Motion. 29 indexed citations

8.

Kong, Chen, Rui Zhu, Hamed Kiani Galoogahi, & Simon Lucey. (2016). Structure from Category: A Generic and Prior-Less Approach. 2. 296–304. 11 indexed citations

9.

Lin, Dahua, Sanja Fidler, Chen Kong, & Raquel Urtasun. (2015). Generating Multi-sentence Natural Language Descriptions of Indoor Scenes. 93.1–93.13. 11 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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