Stereo St in addition to ard stereo geometry St in addition to ard stereo geometry Stereo matching

Stereo St in addition to ard stereo geometry St in addition to ard stereo geometry Stereo matching www.phwiki.com

Stereo St in addition to ard stereo geometry St in addition to ard stereo geometry Stereo matching

Hospe,, Morning Drive Co-Host has reference to this Academic Journal, PHwiki organized this Journal Stereo matching Class 10 Read Chapter 7 http://cat.middlebury.edu/stereo/ Tsukuba dataset Stereo St in addition to ard stereo geometry Stereo matching Correlation Optimization (DP, GC) General camera configuration Rectification Plane-sweep St in addition to ard stereo geometry pure translation along X-axis

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St in addition to ard stereo geometry Stereo matching Search is limited to epipolar line (1D) Look as long as most similar pixel Aggregation Use more than one pixel Assume neighbors have similar disparities Use correlation window containing pixel Allows to use SSD, ZNCC, Census, etc.

Comparing image regions Compare intensities pixel-by-pixel I(x,y) I´(x,y) Sum of Square Differences Dissimilarity measures Comparing image regions Compare intensities pixel-by-pixel I(x,y) I´(x,y) Zero-mean Normalized Cross Correlation Similarity measures Comparing image regions Compare intensities pixel-by-pixel I(x,y) I´(x,y) Census Similarity measures (Real-time chip from TYZX based on Census) only compare bit signature

Aggregation window sizes Small windows disparities similar more ambiguities accurate when correct Large windows larger disp. variation more discriminant often more robust use shiftable windows to deal with discontinuities (Illustration from Pascal Fua) Occlusions (Slide from Pascal Fua)

Real-time stereo on GPU Computes Sum-of-Square-Differences (use pixelshader) Hardware mip-map generation as long as aggregation over window Trade-off between small in addition to large support window (Yang in addition to Pollefeys, CVPR2003) 290M disparity hypothesis/sec (Radeon9800pro) e.g. 512x512x36disparities at 30Hz GPU is great as long as vision too! Exploiting scene constraints Ordering constraint 1 2 3 4,5 6 1 2,3 4 5 6 2 1 3 4,5 6 1 2,3 4 5 6 surface slice surface as a path occlusion right occlusion left

Uniqueness constraint In an image pair each pixel has at most one corresponding pixel In general one corresponding pixel In case of occlusion there is none Disparity constraint surface slice surface as a path bounding box disparity b in addition to constant disparity surfaces Stereo matching Constraints epipolar ordering uniqueness disparity limit Trade-off Matching cost (data) Discontinuities (prior) Consider all paths that satisfy the constraints pick best using dynamic programming

Hierarchical stereo matching Downsampling (Gaussian pyramid) Disparity propagation Allows faster computation Deals with large disparity ranges Disparity map image I(x,y) image I´(x´,y´) Disparity map D(x,y) (x´,y´)=(x+D(x,y),y) Example: reconstruct image from neighboring images

Energy minimization (Slide from Pascal Fua) Graph Cut (Slide from Pascal Fua) (general as long as mulation requires multi-way cut!)

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Simplified graph cut (Boykov et al ICCV‘99) (Roy in addition to Cox ICCV‘98) Stereo matching with general camera configuration

Image pair rectification Planar rectification Bring two views to st in addition to ard stereo setup (moves epipole to ) (not possible when in/close to image) Distortion minimization (uncalibrated)

Next class: structured light

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