Research
I am broadly interested in Computer Vision, Computer Graphics and, Machine Learning.
I am particularly interested in Problems at their Intersection.
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Neural Field Convolutions by Repeated Differentiation
Ntumba Elie, Nsampi,
Adarsh Djeacoumar,
Hans-Peter Seidel,
Tobias Ritschel,
Thomas Leimkühler,
ACM TOG (SIGGRAPH Asia), 2023
Project,
Paper
We introduce an algorithm to perform efficient continuous convolution of neural fields.
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SIDNet: Learning Shading-aware Illumination Descriptor for Image Harmonization
Zhongyun Hu, Ntumba Elie Nsampi,Xue Wang,
Qing Wang
IEEE TETCI, 2023
Project,
Paper
A new framework, with the first image harmonization dataset that has shading variations.
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Physically Inspired Neural Rendering for any-to-any Relighting
Zhongyun Hu,
Ntumba Elie, Nsampi,
Xue Wang,
Qing Wang
IEEE Transaction on Image Processing, 2022
Paper
We decompose the any-to-any relighting problem, into three sub-problems and propose three networks to solve each one independently.
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Neural Shading Field for Image Harmonization
Zhongyun Hu, Ntumba Elie, Nsampi,Xue Wang,
Qing Wang
Arxiv, 2021
Paper /
Code (Coming soon)
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Learning Exposure Correction Via Consistency Modeling
Ntumba Elie, Nsampi,
Zhongyun Hu,
Qing Wang
BMVC, 2021
Paper /
Code
We constrain a deep network to learn an exposure-invariant representation, such that images of different exposure degradation level result in the same representation.
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Depth Guided Image Relighting Challenge
Ntumba Elie, Nsampi,
Zhongyun Hu,
Qing Wang
Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021
Paper
We propose a shadow guidance network, which when plugged into an any-to-any relighting pipeline improves the quality of generated shadows.
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Thanks to Jon Barron for the website template.
Last updated September 2023.
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