Katherine  Xiong

Katherine Xiong

Scholar Title

MIT EECS | Mason Undergraduate Research and Innovation Scholar

Research Title

Deep Image Prior in 3-D





Research Areas
  • Computer Graphics and Vision

Fredo Durand


Recent studies showed that the structure of convolutional neural networks (CNNs) can capture significant image statistics prior to any learning, allowing one to accurately generate and restore an image using a randomly initialized CNN by early-stopping during training. This method is much more time and memory efficient than typical methods that train CNNs to learn realistic image priors from large datasets of input-output image pairs. This project focuses on extending that idea, called Deep Image Prior, to 3-D space. The goal is to design a deep CNN that uses a decoder network as an implicit prior to generate a voxel grid for a scene using only a few input images and no prior training. If successful, we can use the model to simplify 3-D image tasks like novel view synthesis and upsampling.


I am excited to delve deeper into the field of computer graphics and explore the applications of machine learning to image generation tasks. Through SuperUROP, I hope to apply the knowledge I have gained from my machine learning and algorithms classes to a meaningful, long-term research project. Additionally, I hope to learn how to identify research problems and communicate results effectively.

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