Minghua Liu: h-index, Total Citations, and Citation Map
Minghua Liu's h-index is 21 (22 i10-index, 4,714+ total citations across 38+ publications) according to Google Scholar as of June 2026. Minghua Liu is affiliated with Hillbot.
Minghua Liu is a researcher affiliated with Hillbot, specializing in 3D Vision, Embodied AI. Their work has been cited 4,714 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Minghua Liu's Citation Metrics
Bibliometric impact based on 38 indexed publications.
- H-Index
- 21
- i10-Index
- 22
- Total Citations
- 4,714
- Citing Countries
- 60
As of June 2026.
Minghua Liu has an h-index of 21 and 4,714 total citations across 38 publications, with research cited by institutions in 60 countries.
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Global Impact Map
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Top Cited Works
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Sapien: A simulated part-based interactive environment
2020879
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.
206 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher established a foundational simulated part-based interactive environment, subsequently advancing the field with training-free articulated object generation and 3D feature fields for part segmentation.
The researcher pioneered efficient neural architectures for dense point cloud completion and single-image 3D reconstruction, establishing foundational methods widely adopted by the independent computer vision community.
The researcher developed foundational methods for task and path planning in multi-agent pickup and delivery systems, establishing a widely adopted framework for coordinating autonomous agents.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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