I am a computer science Ph.D. candidate at the Harvard School of Engineering and Applied Sciences, working with Todd Zickler. I am currently interning at Google Research, working with Jon BarronPratul Srinivasan, and Ben Mildenhall.

Research Interests:

I am interested in computer vision algorithms that are informed by geometry and physics. By incorporating geometric and physical constraints into differentiable architectures for visual inference, I aim to improve the efficiency and generalizability of artificial vision systems.


Field of Junctions: Extracting Boundary Structure at Low SNR
Dor Verbin, Todd Zickler
arXiv, 2020
By modeling each patch in an image as a generalized junction, our model uses concurrencies between different boundary elements such as junctions, corners, and edges, and manages to extract boundary structure from extremely noisy images where previous methods fail.
Chair SFT
Toward a Universal Model for Shape from Texture
Dor Verbin, Todd Zickler
CVPR, 2020
We formulate the shape from texture problem as a 3-player game. This game simultaneously estimates the underlying flat texture and object shape, and it succeeds for a large variety of texture types.
SFT Uniqueness
Unique Geometry and Texture from Corresponding Image Patches
arXiv, 2020
We present a simple condition for the uniqueness of a solution to the shape from texture problem. We show that in the general case four views of a cyclostationary texture satisfy this condition and are therefore sufficient to uniquely determine shape.