Isabella Liu: h-index, Total Citations, and Citation Map
Isabella Liu's h-index is 9 (8 i10-index, 480+ total citations across 12+ publications) according to Google Scholar as of May 2026. Isabella Liu is affiliated with University of California, San Diego.
Isabella Liu is a researcher affiliated with University of California, San Diego, specializing in Embodied AI, Vision and Graphics. Their work has been cited 480 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Isabella Liu's Citation Metrics
Bibliometric impact based on 12 indexed publications.
- H-Index
- 9
- i10-Index
- 8
- Total Citations
- 480
- Citing Countries
- 27
As of May 2026.
Isabella Liu has an h-index of 9 and 480 total citations across 12 publications, with research cited by institutions in 27 countries.
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Global Impact Map
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Top Cited Works
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Tensoir: Tensorial inverse rendering
2023233
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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.
104 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher pioneered tensorial inverse rendering methods, establishing a foundational framework for decomposing scene properties that has significantly influenced subsequent research in 3D reconstruction and illumination analysis.
The researcher pioneered high-quality 3D mesh generation and articulated object rigging, establishing a foundational framework for template-free, diffusion-based 3D asset creation widely adopted by independent scholars.
The researcher developed Activezero, a mixed-domain learning framework for active stereovision that eliminates the need for manual annotation, subsequently extending this zero-annotation approach to depth completion.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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