Hao Su

15.9k citations
46 papers · 878 indexed · h-index 17

Hao Su

44 papers receiving 862 citations

Peers

Hao Su
Comparison fields: 5 of 119
  • Computer Graphics and Computer-Aided Design 88
  • Computer Vision and Pattern Recognition 291
  • Geology 45
  • Computational Mechanics 124
  • Hepatology 36
Replace Zhe Zhu with:
Zhe Zhu China
Chaoyi Zhang Australia
Yu Qiao China
Hua Zhong China
Tianye Li China
Junli Chen China
Takeshi Masuda Japan
David Meyers United States
Hao Su relative to Zhe Zhu China Zhe Zhu's profile →
Citations per field
00.5×1.5×2.1×
Zhe Zhu · 1×
Citations per year

Countries citing papers authored by Hao Su

Since Specialization
Citations

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

Fields of papers citing papers by Hao Su

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Hao Su, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Hao Su Line = papers co-authored together Hao Su links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 202410
2 20241
3 20245
4 20244
5 202229
6 202226
7 202166
8 202159
9
State Alignment-based Imitation Learning
20202
10 202020
11
Mapping State Space using Landmarks for Universal Goal Reaching
20193
12
S4G: Amodal Single-view Single-Shot SE(3) Grasp Detection in Cluttered Scenes.
20198
13
Adversarial point perturbations on 3D objects
20196
14 20194
15 20191
16 20171
17
FPNN: Field Probing Neural Networks for 3D Data
201677
18 20160
19 201340
20 20042

About Hao Su

Hao Su is a scholar working on Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition, Media Technology, Artificial Intelligence and Instrumentation, having authored 46 papers that have together received 878 indexed citations. Recurring topics across this work include Computer Graphics and Visualization Techniques (5 papers), Domain Adaptation and Few-Shot Learning (4 papers), Human Pose and Action Recognition (4 papers), Advanced Vision and Imaging (4 papers), 3D Shape Modeling and Analysis (4 papers), Reinforcement Learning in Robotics (3 papers), Advanced Neural Network Applications (3 papers) and Image Enhancement Techniques (3 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (88 citations), Computer Vision and Pattern Recognition (291 citations), Geology (45 citations), Computational Mechanics (124 citations) and Hepatology (36 citations). Hao Su has collaborated with scholars based in China, United States and Dominican Republic. Frequent co-authors include J K Yee, Leonidas Guibas, Yan Ji, Chuang Gan, Boqing Gong, Charles R. Qi, Yangyan Li, Sören Pirk, Anpei Chen and Weiliang Shen. Their work appears in journals such as Pharmacology, Biotechnology and Bioengineering, Frontiers in Cell and Developmental Biology, Journal of Neurosurgery Spine and Vacuum.

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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