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M 15338 : Depth Map Estimation Software version 2

M 15338 : Depth Map Estimation Software version 2. Olgierd Stankiewicz Krzysztof Wegner team supervisor: Marek Domański Chair of Multimedia Telecommunications and Microelectronics Poznan University of Technology, Poland. April, 27th 2008, Archamps. Outline. Depth map quality measurement

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M 15338 : Depth Map Estimation Software version 2

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  1. M15338: Depth Map Estimation Software version 2 Olgierd StankiewiczKrzysztof Wegnerteam supervisor: Marek Domański Chair of Multimedia Telecommunications and MicroelectronicsPoznan University of Technology, Poland April, 27th 2008, Archamps

  2. Outline • Depth map quality measurement • Ground-truth map • View resynthesis • View synthesis tool • Depth map estimation tools • Belief Propagation based estimation • Accuracy refinement by mid-level hypothesis • Summary

  3. Depth map quality • Commonly used: ‘Bad-Pixels’ • Miss information about error magnitude and energy • Requires ground-truth disparity map

  4. Depth map quality • NBP-SAD (Normalized Bad PixelSAD) • NBP-SSD (Normalized Bad Pixel SSD) • Still, requires ground-truth disparity map

  5. Depth map quality measurement by view resynthesis • End-user never sees depth-map • Resynthesis • No standarized method • Tool employs straight-forward method • PSNR (Peak Signal-to-Noise Ratio)of resynthesized view as quality measure

  6. Bad-Pixels vs PSNR

  7. View synthesis tool • Simple and straight-forward • For linearly positioned stereo pairs only • Two disparity maps and corresponding reference views • Weighting of pixels from side-views, translated according to their disparity

  8. View synthesis tool

  9. Belief Propagation based depth estimation tool • Alternative for Hierarchical-Shape Adaptive Block Matching • Employs message passing for optimization of disparity map • hierarchical processing in layers • Pixel differences (1-point SAD) used as observations

  10. Message passing in Belief Propagation

  11. Message in Belief Propagation Single message contains information about all possible disparities

  12. Hierarchical processing in Belief Propagation Lower resolution Higher resolution from the lowest resolution to the full resolution in coarse-to-fine manner

  13. Belief propagation Vpq(xp,xq) – transition cost in node q between disparity xp and xq insisted by nodeł pVp(xp) – observation in node p about disparity xp(SAD value) mpq(xq) – message from node p to q about disparity xq

  14. Belief propagation • Pot model • Simpleand computationally efficient • . • Stable beliefs are prefered

  15. Belief propagation results 1th iteration 20 iterations 300 iterations Middlebury test results – 1,65% of bad-pixelsBest Middlebury algorithm – 0,88% of bad-pixels

  16. Bad-Pixels vs PSNR

  17. Accuracy refinement by mid-level hypothesis • Low computational cost • Improves accuracy of disparity map (number of disparity levels) • Spatial resolution unchanged • Focuses on unit-step edges in disparity map

  18. Unit-step edges

  19. Mid-level hypothesis Hypothesis spread along unit-step edges

  20. Refinement by mid-level hypothesis Pixel accurate disparity (1x) After refinement (8x)

  21. Works over untextured regions

  22. Results

  23. Summary • New version of experimental Depth Estimation software • Quality measurement problem with respect to multi-view applications • Simple view resynthesis tool • Belief Propagation depth estimation tool • Novel technique for accuracy refinement

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