Sujit Kuthirummal

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Hi, I am Sujit, a PhD. candidate in the Department of Computer Science at Columbia University. My advisor is Prof. Shree Nayar. I am affiliated with the Columbia Automated Vision Environment (CAVE).

Welcome to my share of the Internet, my home in the electronic world.

My current work involves developing novel imaging systems that capture new kinds of visual information useful for both computer vision and computer graphics applications.

Flexible Imaging for Capturing Depth and Controlling Field of View and Depth of Field
Doctoral Thesis, 2009.
[ PDF ]

Recent Projects:

Flexible Depth of Field Photography,     ECCV 2008, PAMI

[ PDF (ECCV) ]    [ PDF (PAMI Preprint) ]    [ Project Page ]
Priors for Large Photo Collections and What they Reveal about Cameras,     ECCV 2008

[ PDF ]    [ Slides ]     [ Project Page ]
Flexible Mirror Imaging,     ICCV Workshop on Omnidirectional Vision: OMNIVIS 2007

The field of view of a traditional camera has a fixed shape, which limits how we compose scene elements into an image. We present a novel imaging system with a flexible field of view - the size and shape of the field of view can be varied to achieve the desired scene composition.

[ PDF ]    [ Project Page ]     [ Slides ]     [ Slides w/ Videos]    
Multiview Radial Catadioptric Imaging for Scene Capture,     SIGGRAPH 2006

A picture taken by a conventional camera captures the scene from the camera's single viewpoint. In many applications in computer vision and computer graphics, it is desirable to capture the scene from a large number of viewpoints. In this project, we explore a class of imaging systems, called radial imaging systems, that capture a scene from circular loci of virtual viewpoints, in addition to the viewpoint of the camera. We have built radial imaging systems that can, from a single image, recover the frontal 3D structure of an object, generate the complete texture map of a convex object, and estimate the parameters of an analytic BRDF for an isotropic material. In addition, one of our systems can recover the complete geometry of a convex object by capturing only two images.

[ PDF ]     [ Project Page ]     [ Slides ]     [ Slides w/ Videos]    

Click here for the full list of my publications

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