[Colloquium] Talks at TTIC: Qixing Huang, Stanford

[Dawn Ellis]

When:     Thursday, February 20th at 11am

Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room #526

Speaker:  Qixing Huang, Stanford

Title:       Fine-grained shape classification and beyond

Thanks to innovation in 3D scanning and modeling technologies, large
collections of 3D shapes become widely available. For example, the popular
3D Warehouse repository contains millions of man-made 3D shapes, and the
size continues to grow at a high rate. To make these big geometric datasets
useful, we typically start from classifying them into different classes. So
far most existing works have focused on classifying shapes into different
high-level categories, e.g., cars, chairs, desks, etc. However, for large
shape collections, the shapes within each category still exhibit
significant variation. For example, chair models from the Trimble 3D
Warehouse contain dozens of sub-classes, including chairs-with-arms, swivel
chairs, rocking chairs, etc.  These fine-grained classes contain rich
semantic information that can benefit a variety of applications such as
product search, browsing and exploration of shape variability, and
interactive shape modeling.


In this talk we consider the problem of classifying a collection of shapes
of the same category into fine-grained classes. We start from understanding
the challenges of fine-grained shape classification, including (i) lacking
canonical domains to compare shapes, (ii) subtle geometric features that
characterize each class, and (iii) the fact that only sparse and noisy
labels are available. We then discuss a semi-supervised classification
pipeline which combines joint shape alignment in common spaces, joint
class-specific distance metric learning and graph-based semi-supervised
learning. The performance of this pipeline turns to be much better than
existing shape classification techniques. We will also discuss various
applications of extracting fine-grained classes such as shape exploration
and shape-driven image-based modeling.

 BIO:

Qixing Huang is a Post-doc researcher at the computer science department of
Stanford University.  His main research interests are in organizing large
collections of geometric data and exploring various data-driven
applications in analysis, animation, modeling and visualization. Qixing
Huang received the PHD degree from Stanford University in 2012, working
with Prof. Leonidas Guibas.  He is the recipient of Mr. and Mrs. Chin-Nan
Chen Stanford Graduate Fellowship from 2008-2011, and the recipient of the
Best Paper Award at the conference Symposium on Geometry Processing 2013.

Host: Gregory Shakhnarovich, gregory at ttic.edu

-- 
*Dawn Ellis*
Administrative Coordinator,
Bookkeeper
773-834-1757
dellis at ttic.edu

TTIC
6045 S. Kenwood Ave.
Chicago, IL. 60637
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