Bo Yang: h-index, Total Citations, and Citation Map
Bo Yang's h-index is 26 (36 i10-index, 6,946+ total citations across 55+ publications) according to Google Scholar as of June 2026. Bo Yang is affiliated with Asst Prof, Hong Kong Polytechnic University; DPhil, University of Oxford.
Bo Yang is a researcher affiliated with Asst Prof, Hong Kong Polytechnic University; DPhil, University of Oxford, specializing in Machine Learning, 3D Computer Vision, Robot Learning. Their work has been cited 6,946 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Bo Yang's Citation Metrics
Bibliometric impact based on 55 indexed publications. Of these, 18 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 26
- i10-Index
- 36
- Total Citations
- 6,946
- Citing Countries
- 56
As of June 2026.
Bo Yang has an h-index of 26 and 6,946 total citations across 55 publications, with research cited by institutions in 56 countries.
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We've mapped 5,000 of 6,946 citations for Bo Yang
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Global Impact Map
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RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
20202,864
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
569 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher developed efficient semantic segmentation methods for large-scale point clouds, establishing a foundational framework that enabled subsequent advancements in 3D registration and unsupervised learning.
The researcher pioneered general radiance fields for 3D representation, subsequently extending this framework to dynamic physics learning and scene geometry decomposition.
The researcher pioneered adversarial learning for single-view 3D reconstruction, establishing a foundational framework that subsequent work extended to dense reconstruction and non-rigid physical dynamics.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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