[4eyes] Fwd: [grads] PhD Defense - Christopher Sweeney - Friday 1/22/16

[Chris Sweeney]

Hi all,

Just a reminder that my defense is tomorrow afternoon. Please come by if
you are free -- I have ordered some nice cheeses and meats for you to snack
on :)

Chris


---------- Forwarded message ---------
From: Jillian Title <jillian.title at cs.ucsb.edu>
Date: Thu, Jan 14, 2016 at 2:06 PM
Subject: [grads] PhD Defense - Christopher Sweeney - Friday 1/22/16
To: <grads at cs.ucsb.edu>, <faculty at cs.ucsb.edu>, <lecturers at cs.ucsb.edu>, <
research at lists.cs.ucsb.edu>, <colloquium at lists.connect.ucsb.edu>


PhD Defense
*Christopher Sweeney*
Friday, January 22nd at 2pm
HFH 1132

*Committee: *Tobias Höllerer (Co-Chair), Matthew Turk (Co-Chair), Pradeep
Sen, Noah Snavely

*Title:* Modeling and Calibrating the Distributed Camera

*Abstract: *

Structure-from-Motion (SfM) is a powerful tool for computing 3D
reconstructions from images of a scene and has wide applications in
computer vision, scene recognition, and augmented and virtual reality.
Standard SfM pipelines make strict assumptions about the capturing devices
in order to simplify the process for estimating camera geometry and 3D
structure. Specifically, most methods require monocular cameras with known
focal length calibration. When considering large-scale SfM from internet
photo collections, EXIF calibrations cannot used reliably. Further, the
requirement of single camera systems limits the scalability of SfM.

This thesis proposes to remove these constraints by instead considering the
collection of cameras as a "distributed camera" that encapsulates the image
and geometric information of all cameras simultaneously. First, I provide
full generalizations to the relative camera pose and absolute camera pose
problems. These generalizations are more expressive and extend the
traditional single-camera problems to distributed cameras, forming the
basis for a novel hierarchical SfM pipeline that exhibits state-of-the-art
performance on large-scale datasets. Second, I describe two efficient
methods for estimating camera focal lengths for the distributed camera when
calibration is not available. Finally, I show how removing these
constraints enables a simpler, more scalable SFM pipeline that is capable
of handling uncalibrated cameras at scale.

Everyone Welcome!

-- 
Jillian Title
Graduate Advisor
Department of Computer Science
University of California Santa Barbara
Harold Frank Hall 2104
Santa Barbara, CA 93106-5110
(805) 893-4322

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