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Matheus Medeiros de Souza
I am a Ph.D. student at KAUST Computational Imaging Group, working with Prof. Wolfgang Heidrich.
My research is centered on the innovative fusion of deep reconstruction algorithms and end-to-end optics design. This involves developing deep learning models and formulating optimization strategies that incorporate optical elements into the computational loop.
Focusing on the following topics:
Minimalistic cameras design.
Deep learning for optical design.
Computational cameras.
Optics-aware computational photography.
Email  / 
Google Scholar  / 
LinkedIn
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Ph.D. in Computer Science
King Abdullah University of Science and Technology (KAUST), Saudi Arabia
2022 - Present
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M.Sc. in Computer Science
King Abdullah University of Science and Technology (KAUST), Saudi Arabia
2020 - 2022
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B.Sc. in Electrical Engineering
Universidade Federal do Rio Grande do Norte (UFRN), Brazil
2015 - 2019
Graduated with Highest Honors • Best Student Award, Class of 2019
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Research Scientist Intern in Computational Imaging
Meta Reality Labs - Surreal Team
Mentored by Michael Goesele
June 2025 - November 2025
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Latent Space Imaging
Matheus Souza, Yidan Zheng, Kaizhang Kang, Yogeshwar Nath Mishra, Qiang Fu, Wolfgang Heidrich
CVPR 2025,
Code
New paradigm for very low bandwidth image capture based on generative models latent space.
The demonstration of a range of downstream applications on this latent space with real hardware experimentation.
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Limitations of Data-Driven Spectral Reconstruction - An Optics-Aware Analysis
Qiang Fu*, Matheus Souza*, Suhyun Shin, Eunsue Choi, Seung-Hwan Baek, Wolfgang Heidrich
Computational Optical Sensing and Imaging, 2024. Oral Presentation
IEEE TCI
Comprehensive analysis of state-of-the-art data-driven hyperspectral imaging atypical overfitting.
Optical aberrations can provide encoding power if modeled correctly.
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End-to-End Hybrid Refractive-Diffractive Lens Design with Differentiable Ray-Wave Model
Xinge Yang, Matheus Souza, Kunyi Wang, Praneeth Chakravarthula, Qiang Fu, Wolfgang Heidrich
Siggraph Asia 2024. Paper (Arxiv) / Paper (PDF) / Supp (PDF)
Differentiable ray-tracing and wave-propagation model.
End-to-End hybrid refractive-diffractive lenses design with prototypes.
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MetaISP - Exploiting Global Scene Structure for Accurate Multi-Device Color Rendition
Matheus Souza, Wolfgang Heidrich
MetaISP VMV 2023 / Code
CRISPnet: Color rendition ISP net. Paper (Arxiv)
We developed a model for learning multiple commercial ISPs.
Integrating global scene semantics, metadata information, and advanced deep learning techniques.
Collected synthetic and real-world datasets, consisting of RAW-RGB pairs from various devices.
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