Brandon Y. Feng

I am an incoming Assistant Professor of Computer Science at UT Austin. Prior to UT, I was a Postdoctoral Associate at MIT CSAIL, working with William T. Freeman. I did my PhD in Computer Science at the University of Maryland, advised by Amitabh Varshney. Before that, I was an undergrad at the University of Virginia, majoring in Computer Science and Statistics.

Select Publications

Exoplanet Imaging via Differentiable Rendering

Exoplanet Imaging via Differentiable Rendering

Seeing new light from exoplanets.

NeuWS: Neural Wavefront Shaping for Guidestar-Free Imaging Through Static and Dynamic Scattering Media

NeuWS: Neural Wavefront Shaping for Guidestar-Free Imaging Through Static and Dynamic Scattering Media

Neural signal representations enable breakthroughs in correcting for severe time-varying wavefront aberrations caused by scattering media.

3D Motion Magnification: Visualizing Subtle Motions with Time-Varying Neural Fields

3D Motion Magnification: Visualizing Subtle Motions with Time-Varying Neural Fields

3D motion magnification allows us to magnify subtle motions in seemingly static scenes while supporting rendering from novel views.

FPM-INR: Fourier Ptychographic Microscopy Image Stack Reconstruction Using Implicit Neural Representations

FPM-INR: Fourier Ptychographic Microscopy Image Stack Reconstruction Using Implicit Neural Representations

Physics-based neural signal representations accelerate real-time 3D refocusing in Fourier ptychographic microscopy and overcome barriers to clinical diagnosis.

Seeing the World Through Your Eyes

Seeing the World Through Your Eyes

CVPR 2024 Oral Presentation Acc. Rate: 0.78%

The only true voyage of discovery would be not to visit strange lands, but to possess other eyes, to behold the universe through the eyes of another.

VIINTER: View Interpolation with Implicit Neural Representations of Images

VIINTER: View Interpolation with Implicit Neural Representations of Images

View interpolation without 3D reconstruction or correspondence.

PRIF: Primary Ray-based Implicit Function

PRIF: Primary Ray-based Implicit Function

Efficient implicit 3D shape representation.

SIGNET: Efficient Neural Representations for Light Fields

SIGNET: Efficient Neural Representations for Light Fields

Brandon Y. Feng, Amitabh Varshney
ICCV 2021 Oral Presentation Acc. Rate: 3.36%

Remarkable compression rates by representing light fields as neural network weights. Simple and compact formulation also supports angular interpolation to generate novel viewpoints.


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