3D object recognition and beyond
- LecturerMin Sun (Ph.D candidate, University of Michigan at Ann Arbor)
Host: Dr. Chu-song Chen - Time2010-06-14 (Mon.) 14:30 – 16:30
- LocationAuditorium 106 at new IIS Building
Abstract
Abstract:
Recognizing object classes and their 3D viewpoints is an important
problem in computer vision. Based on a part-based probabilistic
representation of our CVPR 2009 work, we propose a new 3D object class
model that is capable of recognizing unseen views by pose estimation
and synthesis. We achieve this by using a dense, multiview
representation of the viewing sphere parametrized by a triangular mesh
of viewpoints. The model is trained incrementally using Internet
images labelled with object bounding boxes and categorical labels. Our
method achieves good result on the Savarese et al. 2007 and PASCAL
datasets in object detection. We further explore the possibility of
recognizing 3D objects on a robotic platform. We use the depth sensor
on the robot to train a 3d-encoded object model and show that detection
performance improves even 3d information is given only in training.
Short Bio:
Min Sun graduated from National Chiao Tung University in 2003 with an
Electrical Engineering degree. He received a MS degree from Stanford
University in Electrical Engineering department in 2007. He was a
member of Vision Lab at the Princeton University from 2008 to 2009.
Now he is a PhD student in the Vision Lab at the University of Michigan
at Ann Arbor. His research interests include object recognition, image
understanding, and machine learning. He was a recipient of W. Michael
Blumenthal Family Fund Fellowship and has won the best paper awards
in 3DRR of ICC`'07.