Neural Image-Based Lighting is a computational method in computer graphics that leverages neural networks to model and render realistic illumination effects from environment maps, distinguishing itself from traditional image-based lighting by replacing analytical or sampled lighting computations with learned representations of light transport.
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Publications
135
Citations
14
Authors
3
Institutions
Publications per year
2017–2026
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Parents
Publications
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All-Weather Deep Outdoor Lighting Estimation
Jinsong Zhang, Kalyan Sunkavalli, Yannick Hold-Geoffroy et al. · 2019 · 81 citations
LightingFormer: Transformer-CNN hybrid network for low-light image enhancement
Cong Bi, Wenhua Qian, Jinde Cao et al. · Computers & Graphics · 2024 · 13 citations
All-Weather Deep Outdoor Lighting Estimation
Jinsong Zhang, Kalyan Sunkavalli, Yannick Hold-Geoffroy et al. · arXiv (Cornell University) · 2019 · 10 citations · Full text
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Authors
14
- JZ
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- KS
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- SH
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- JL
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- YH
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1–5 of 14
Institutions
3
Adobe Systems (United States)
San Jose, United States
1 h-index
4 publications
324 citations
ShanghaiTech University
Shanghai, China
1 h-index
5 publications
160 citations
Indian Institute of Information Technology Allahabad
Prayagraj, India
1 h-index
2 publications
24 citations
Southeast University
Nanjing, China
1 publications
13 citations
Yunnan University
Kunming, China
1 publications
13 citations
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Venues
- arXiv (Cornell University)
Repository · 1 publications · 10 citations
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- Pattern Recognition Letters
1 publications · 12 citations
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- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Conference · 1 publications · 32 citations
1
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- Computers & Graphics
1 publications · 13 citations
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