Vidya Narayanan

I am a first year graduate student in the computer science department at Carnegie Mellon University. I am advised by Stelian Coros and Jim McCann. I am broadly interested in fabrication, graphics and visualization.

Before joining CMU, I was a research associate at Disney Research, Pittsburgh advised by Jim McCann. I earned my masters degree at the Indian Institute of Science, focusing on graphics and scientific visualization and was advised by Vijay Natarajan.

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Research Interests

I am interested in computational tools for fabrication, computer graphics, and visualization. I believe existing fabrication machinery such as knitting machines and weaving looms have been largely overlooked as technology that can be used for custom and rapid fabrication much like 3D printers. My current research looks at automatic and semi-automatic tools for fabricating textiles, particularly computational machine knitting.


A compiler for 3D Machine Knitting
James McCann, Lea Albaugh, Vidya Narayanan , April Grow,Wojciech Matusik, Jen Mankoff, Jessica Hodgins
ACM Transactions on Graphics (SIGGRAPH), 2016
paper | project page

Industrial knitting machines can produce finely detailed 3D surfaces but. programming them requires in depth knowledge of low-level knitting operations. In this work, we built a compiler to convert high level design primitives into knitting machine instructions.


Distance between extremum graphs
Vidya Narayanan , Dilip Thomas, Vijay Natarajan
IEEE Pacific Visualization Symposium(PacificVis), 2015
paper | project page

Scientific phenomena are often studied through collections of related scalar fields. Exploration of such data requires a robust distance measure to compare scalar fields for tasks such as identifying key events and establishing correspondence between features in the data. We propose a topological data structure called the complete extremum graph and define a distance measure on it for comparing scalar fields in a feature-aware manner.

Other Projects

Computational String Art

String or pin-thread art is a popular craft that involves winding a string around a set of nails to generate an artifact. An important task in automatic fabrication of such art work is planning the string layout to achieve the target representation. We explored this planning problem for generating string-art from images automatically. Motivated by artists (see Petros Vrellis , Kumi Yamashita ), we built an automatic framework to design such artifacts. Turns out that various people have been looking at similar ideas.

Last updated March 2017.