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Discontinuity Preserving Stereo with Small Baseline Multi-Flash Illumination Rogerio Feris 1, Ramesh Raskar 2, Longbin Chen 1, Karhan Tan 3 and Matthew Turk 1 1 University of California, Santa Barbara 2 Mitsubishi Electric Research Labs 3 Epson Palo Alto Lab

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Introduction Correspondence Problem Stereo Near Depth Discontinuities: - Occlusion Problem - Perspective Distortions - Violation of Smoothness Constraints Passive Versus Active Methods

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Introduction Our Approach: Small Baseline Multi-Flash Illumination - Simple, Inexpensive - Compact, Self-Contained - Discontinuity Preserving

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Depth Edges with Multi-Flash Raskar, Tan, Feris, Yu, Turk – ACM SIGGRAPH 2004

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Bottom Flash Top Flash Left Flash Right Flash Ratio images and directions of epipolar traversal Shadow-Free Depth Edges Shadow-FreeDepth Edges

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Qualitative Depth Map

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Qualitative Depth Sign of Depth Edge - Indicates which side is the foreground and which side is the background Shadow Width - Encodes object relative distances

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Sign of Depth Edge (+) Foreground (-) Background Original Ratio Left Ratio Right Signed Edges

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Shadow Width Bottom Flash Image Ratio Image Plot Along Scanline

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Shadow Width Bottom Flash Image Ratio Image Shadow Width Estimation: Meanshift Segmentation algorithm applied on the ratio image

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Imaging Geometry Object Flash Shadow Camera B z1z1 z2z2 f Shadow Width d

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Qualitative Depth Working on this Equation … Log Depth Difference Shadow Width Gradient-Domain Problem!

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Qualitative Depth 1) Compute Sharp Depth Gradient G = (G h,G v ) Log Depth Difference Sign of depth edge 2) Compute Q by integrating G (Poisson Equation) 3) Qualitative depth map Q = exp(Q)

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Qualitative Depth Useful Prior Information for Stereo !

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Occlusion Map

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Partial Occlusion Problem Object Camera Occlusion AB (Seen by A but not by B)

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Occlusion Bounded by Shadows Object CameraAB Flash Occlusion (Seen by A but not by B)

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Occlusion Bounded by Shadows Object CameraAB Flash Lower Bound Shadow

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Occlusion Bounded by Shadows Object CameraAB Flash Upper Bound Shadow

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Occlusion Bounded by Shadows Object Camera Occlusion AB Average of Upper/Lower Shadow widths Flash

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Occlusion Bounded by Shadows Occlusion Map Left ViewRight View

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Discontinuity Preserving Stereo Matching

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Local Stereo Problem: Shape and size of correlation window - Small Window Ambiguities / Noise - Large Window Problems at Depth Discontinuities Depth Edge Preserving Local Stereo

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Local Stereo Smooth Disparity Delimited by depth edges + Occlusions Correlation Window Problem: Shape and size of correlation window - Small Window Ambiguities / Noise - Large Window Problems at Depth Discontinuities Depth Edge Preserving Local Stereo

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Local Stereo Left View Depth Edges + OcclusionGround Truth Challenging Scene: - Ambiguous patterns, textureless regions, geometrically complex object, thin structures

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Local Stereo Conventional 9x9 Conventional 31x31 Our Approach 31x Conventional Stereo Our Approach

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Global Stereo Global Optimization – Markov Random Field (MAP-MRF) X = {x s } Disparity of each pixel (Hidden) Y = {y s } Matching cost at each disparity (Observed) X3X3 X1X1 X2X2 X7X7 X4X4 X6X6 X5X5 X8X8 y1y1 y2y2

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Global Stereo Global Optimization – Markov Random Field (MAP-MRF) X = {x s } Disparity of each pixel (Hidden) Y = {y s } Matching cost at each disparity (Observed) Data Term Smoothness Term Inference by Belief Propagation [Jian Sun et al, 2003]

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Global Stereo Qualitative Depth Map as Evidence - Used to set the smoothness term - Information propagation is stopped at depth edges - Encourage disparities for neighboring pixels according to depth difference in qualitative map Occlusion Penalty

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Global Stereo Conventional Belief Propagation Our Approach RMS: RMS:

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Conclusions Contributions - Stereo with small baseline illumination - Useful Feature Maps (Qualitative Depth + Occlusion Map) - Enhanced Local and Global Stereo Algorithms Pros / Cons - Robust, Simple, Inexpensive and Compact - Limited to handle outdoor scenes and motion Website (datasets, source code) -

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Thank you ! Multi-Flash Stereo Webpage Four Eyes Lab, UCSB

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Occlusion Bounded by Shadows Occlusion Detection by averaging length of shadows Images taken with light sources surrounding the other camera

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