I spy with my little eye: Learning Optimal Filters for Cross-Modal Stereo under Projected Patterns
Motivation
Wei-Chen Chiu, Ulf Blanke, Mario Fritz http://www.mpi-inf.mpg.de/~walon/files/cross_modal_kinect/ Kinect: active sensing failed on specular, transparent or reflective surface ► compliment with cross-modal stereo between Kinect's RGB and IR camera
Problem
RGB image
IR image
Depth from Kinect
► Previous work from Chiu et al.@BMVC: simple pixel-wise linear combination Pixel-wise re-weighting of RGB channels to be an IR-like image Cross-modal stereo ► under assumption that linear combination of RGB channels can recover sensor response characterisitic of IR camera lambertian surface
RGB IR Cross-modal adaptation
IR-like image
Depth
two slits & grating
light source
Kinect
Fusion
w_r w_g w_b
closed box
Pointcloud
Disparity map
Stereo
w_ir
raw / f tted
• simple pixel-wise re-weighting of IR and RGB seems to be to limited • The projected pattern on IR image required for active depth sensing make interference to compute the cross-modal stereo
Method
Disparity from stereo
rgb
blue
red green blue ir1 ir2 ir3 ir4 rgb mimics ir rgb mimcs ir amplified
ir
green
red
ir
Extend transformation learning from pixel-wise to patch-wise ► linear filters that leverage a pixel neighborhood in all color channels to preserve matches across modalities
Result
► learned filters encompass smoothing, sharpening and edge detection: helpful as prefilters in stereo matching and alleviate problems with IR pattern
Disparity map on unfiltered pairs
Disparity map on patch-filtered pairs
Perform evaluation on object segmentation task from 3D point clouds. ► comparing to avg precision 46% of Kinect depth estimate, our method improve over 20% and is on par with previous work under IR-projector-off which has practical issues.
kinect only (AP = 46.0)
0.9
spectral (AP = 64.6) best for IR-on (AP = 69.0)
0.8
proposed method (AP = 71.5) best for IR-off (AP = 72.5)
I spy with my little eye: Learning Optimal Filters for ...
best for IR-on (AP = 69.0) proposed method (AP = 71.5) best for IR-off (AP = 72.5). Perform evaluation on object segmentation task from 3D point clouds.
With the introduction of the Kinect as a gaming inter- faces, its broad commercial accessibility and high quality ... for improvement over basic patch-based differences. In our case the need for learning is even more ..... Transparent object detectio
AbstractâData representation methods related to ICA and ... Helsinki Institute for Information Technology, Dept. of Computer Science,. University of Helsinki.
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