Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm

Session WedGT1 Pegaso A Wednesday, October 10, 2012, 17:30–18:30
Stereo Vision
Chair Il Hong Suh, Hanyang Univ.
Co-Chair Andreas Zell, Univ. of Tübingen
17:30–17:45 WedGT1.1
A New Feature Detector and Stereo Matching
Method for Accurate High-Performance Sparse
Stereo Matching
Konstantin Schauwecker and Andreas Zell
Department Cognitive Systems, University of Tübingen, Germany
Reinhard Klette
Computer Science Department, The University of Auckland, New Zealand
• Computationally efficient sparse stereo
matching system, achieving processing
rates above 200 frames per second on a
commodity dual-core CPU.
• Although features are matched sparsely, a
dense consistency check is applied, which
drastically decreases the number of false
matches.
• A new FAST-based feature detector is
used, which has a less clustered feature
distribution and leads to an improved
matching performance.
18:00–18:15 WedGT1.3
Can Stereo Vision replace a Laser Rangefinder?
M. Antunes, J.P. Barreto, C. Premebida and U. Nunes
Department of Electrical and Computer Engineering
Institute of Systems and Robotics
University of Coimbra, Portugal
• We propose Stereo Rangefinding (SRF)
for estimating depth along virtual scan
planes
• The SymStereo framework is used for
quantifying the likelihood of pixel
correspondences using induced symmetry
• The depth estimates of SRF are compared
against the data provided by a LRF
• We show that passive stereo can be an
alternative to LRF in certain robotic
applications
Our paper shows that it is
possible to recover the profile cut
(green) directly from two cameras
17:45–18:00 WedGT1.2
Real-time Velocity Estimation Based on
Optical Flow and Disparity Matching
Dominik Honegger, Pierre Greisen, Lorenz Meier,
Petri Tanskanen and Marc Pollefeys
ETH Zürich Switzerland
• We present an image-based real-time
metric velocity sensor for mobile robot
navigation.
• An FPGA-based stereo camera platform
combines optical flow and disparity values
at 127 fps and 376*240 resolution.
• Radial undistortion, image rectification,
disparity estimation and optical flow are
performed on a single FPGA.
• Suited for MAVs due to low-weight, lowpower
and low-latency.
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
–184–
System Setup, Rectified Input
Image, Disparity Map,
Flow Field
18:15–18:30 WedGT1.4
Dependable Dense Stereo Matching by Both
Two-layer Recurrent Process and Chaining
Search
Sehyung Lee, Youngbin Park and Il Hong Suh
Department of Electronics and Computer Engineering, Hanyang University,
Korea
• We propose a recurrent two-layer process
and chaining search for dense stereo
matching.
• The disparity map is calculated through
the iterative integration of pixel and region
layers.
• To estimate the precise disparities in
occluded regions, reliable disparities are
propagated by the chaining search.
• To test our algorithm, it was compared
with two outstanding algorithms in
Middlebury benchmark using Gaussian
noisy images.
The first row shows the images with
varying PSNR. The second, third, and
fourth rows show the results of the CVF,
the DBP, and the proposed method.