Brandon Y. Feng

I am a Ph.D. Candidate in Computer Science at the University of Maryland advised by Prof. Amitabh Varshney. My research lies at the intersection of graphics, vision, and imaging. I develop machine learning algorithms for image and 3D data processing, with applications ranging from mixed reality to natural science. My goal is to enhance our ability to perceive, represent, and create information with positive societal impact.

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Publications

VIINTER: View Interpolation With Implicit Neural Representations of Images

VIINTER: View Interpolation With Implicit Neural Representations of Images

Brandon Y. Feng, Susmija Jabbireddy, Amitabh Varshney
SIGGRAPH Asia, 2022

View interpolation without 3D reconstruction or correspondence.

PRIF: Primary Ray-based Implicit Function

PRIF: Primary Ray-based Implicit Function

Brandon Y. Feng, Yinda Zhang, Danhang Tang, Ruofei Du, Amitabh Varshney
ECCV, 2022

Efficient implicit 3D shape representation.

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Benchmarking AlphaFold for Protein Complex Modeling Reveals Accuracy Determinants

Benchmarking AlphaFold for Protein Complex Modeling Reveals Accuracy Determinants

Rui Yin, Brandon Y. Feng, Amitabh Varshney, Brian G. Pierce
Protein Science, 2022

Interesting findings from evaluating AlphaFold2 on protein-protein docking, highlighting areas for future development using deep learning methods.

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TurbuGAN: An Adversarial Learning Approach to Spatially-Varying Multiframe Blind Deconvolution with Applications to Imaging Through Turbulence

TurbuGAN: An Adversarial Learning Approach to Spatially-Varying Multiframe Blind Deconvolution with Applications to Imaging Through Turbulence

Brandon Y. Feng*, Mingyang Xie*, Christopher A. Metzler
Preprint

Training GAN only on blurry images from a single scene to recover a sharp image without estimating the blur kernels or acquiring a large labelled dataset.

SIGNET: Efficient Neural Representations For Light Fields

SIGNET: Efficient Neural Representations For Light Fields

Brandon Y. Feng, Amitabh Varshney
ICCV, 2021 Oral Presentation

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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GazeChat: Enhancing Virtual Conferences with Gaze-aware 3D Photos

GazeChat: Enhancing Virtual Conferences with Gaze-aware 3D Photos

Zhenyi He, Keru Wang, Brandon Y. Feng, Ruofei Du, Ken Perlin
UIST, 2021

Visualizing "who's looking at who" from static profile images as people always turn off their videos. Synthesized eye gazes are obtained by leveraging a general-purpose neural network.

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Deep Depth Estimation on 360-Degree Images With a Double Quaternion Loss

Deep Depth Estimation on 360-Degree Images With a Double Quaternion Loss

Brandon Y. Feng, Wangjue Yao, Zheyuan Liu, Amitabh Varshney
3DV, 2020

Training neural networks for 360° monocular depth and normal estimation. Proposed a novel approach of combining depth and normal as a double quaternion during loss computation.

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A Self-Adaptive Network for Multiple Sclerosis Lesion Segmentation From Multi-Contrast MRI With Various Imaging Sequences

A Self-Adaptive Network for Multiple Sclerosis Lesion Segmentation From Multi-Contrast MRI With Various Imaging Sequences

Brandon Y. Feng, Huitong Pan, Craig H. Meyer, Xue Feng
ISBI, 2019

Neural networks with multimodal input disproportionately rely on certain modality while ignoring the rest. Developed a new strategy to overcome such bias and adapt to missing modalities.

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