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875: Recent Advances in Geometric Computer Vision & Recognition Jan-Michael Frahm Spring 2014

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Introductions 2

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Grade Requirements Presentation of 2 papers in class 30 min talk, 10 min questions Papers for selection must come from: top journals: IJCV, PAMI, CVIU, IVCJ top conferences: CVPR (2010,2011), ICCV (2011), ECCV (2010), approval for all other venues is needed Final project evaluation, extension of a recent method from the above 3

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Grading 20% first presentation 20% second presentation 30% final project 30% attendance & class participation 4

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Schedule Jan. 7 th, Introduction Jan 7 th, Uncertainty in Stereo (guest Philippos Mordohai) (substitute for Jan 13 th class) Jan 15 th, Large-scale image localization basic concepts, First paper selection (Large –scale localization) Jan 20 th, MLK holiday no class Jan 22 nd -29th, Large-scale localization basic concepts Feb. 3 rd, 1. round of presentations starts Mar. 10 th, 12 th Spring break (no class) Mar. 17 th, Modeling dynamic objects/scenes basic concepts, Second paper selection, final project definition Mar. 19 st, Modeling dynamic objects Mar. 24 th, 2. round of presentations starts Apr. 21 st, 23 rd, final project presentation 5

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How to give a great presentation Structure of the talk: Motivation (motivate and explain the problem) Overview Related work (short concise discussion) Approach Experiments Conclusion and future work 6

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How to give a great presentation Use large enough fonts 5-6 one line bullet items on a slide max Keep it simple No complex formulas in your talk Bad Powerpoint slides How to for presentations 7

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How to give a great presentation Abstract the material of the talk provide understanding beyond details Use pictures to illustrate find pictures on the internet create a graphic (in ppt, graph tool) animate complex pictures 8

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How to give a good presentation Avoid bad color schemes no red on blue looks awful Avoid using laser pointer (especially if you are nervous) Add pointing elements in your presentation Practice to stay within your time! Don’t rush through the talk! 9

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Brush up on Stereo Reconstruction 10

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Stereo Extraction of 3D information from 2D images 11 Images3D Point Cloud Stereo

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Binocular stereo Given a calibrated binocular stereo pair, fuse it to produce a depth image Humans can do it Stereograms: Invented by Sir Charles Wheatstone, 1838

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Depth Recovery by Stereo reference imagematching image Depth d1d1 d2d2 d3d3 d4d4 d5d5 d6d6 d7d7 d8d8 d9d9 Search Space Epipolar line 13

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Depth Recovery from Stereo reference imagematching image Depth d1d1 d2d2 d3d3 d4d4 d5d5 d6d6 d7d7 d8d8 d9d9 Search Space Epipolar line depth Pixel similarity: measured by color differences Matching Cost 14 Ground Truth Pixel Matching Depth Map

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Matching criteria Raw pixel values (correlation) Band-pass filtered images [Jones & Malik 92] “ Corner ” like features [Zhang, …] Edges [many people…] Gradients [Seitz 89; Scharstein 94] Rank statistics [Zabih & Woodfill 94] Intervals [Birchfield and Tomasi 96] Overview of matching metrics and their performance: H. Hirschmüller and D. Scharstein, “Evaluation of Stereo Matching Costs on Images with Radiometric Differences”, PAMI 2008 slide: R. Szeliski

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Adaptive Weighting Boundary Preserving More Costly

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Simplest Case: Parallel images Image planes of cameras are parallel to each other and to the baseline Camera centers are at same height Focal lengths are the same slide: S. Lazebnik

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Simplest Case: Parallel images Image planes of cameras are parallel to each other and to the baseline Camera centers are at same height Focal lengths are the same Then, epipolar lines fall along the horizontal scan lines of the images slide: S. Lazebnik

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Essential matrix for parallel images R = I t = (T, 0, 0) Epipolar constraint: t x x’

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Essential matrix for parallel images Epipolar constraint: R = I t = (T, 0, 0) t x x’

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Aggregation Structure depth Matching Cost Pixelwise Costs 21 Search Space

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Aggregation Structure Cost Volume Cost aggregation: cutting the cost volume. 22 Search Space

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Aggregation Structure Cost Volume Fronto-Parallel Plane 23 Treat neighbors equally Cost of the center pixel Costs of neighboring pixels Sum of Absolute Differences (SAD) Depth Map

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Aggregation Structure Adaptive Weight Yoon and Kweon, PAMI 2006 Depth MapCost Volume 24 Color differences Spatial distances Weighted cost of the center pixel Weighted costs of neighboring pixels

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Aggregation Structure Adaptive Weight Depth Map Oriented Plane Cost Volume Lu et al., CVPR

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Stereo: epipolar geometry Match features along epipolar lines viewing ray epipolar plane epipolar line slide: R. Szeliski

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Your basic stereo algorithm For each epipolar line For each pixel in the left image compare with every pixel on same epipolar line in right image pick pixel with minimum match cost Improvement: match windows This should look familar... slide: R. Szeliski

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Depth Map Computation Local methods Depth with the minimum cost Complexity: Global methods Pairwise interactions Complexity: Scharstein and Szeliski, “A taxonomy and evaluation of dense two- frame stereo correspondence algorithms", IJCV 2002 Image Resolution : the total number of pixels 28 N pixels aN pixels bN pixels

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Depth from disparity f xx’ Baseline B z OO’ X f Disparity is inversely proportional to depth!

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Depth Sampling Depth sampling for integer pixel disparity Quadratic precision loss with depth!

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Depth Sampling Depth sampling for wider baseline

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Depth Sampling Depth sampling is in O(resolution 6 )

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Finding correspondences apply feature matching criterion (e.g., correlation or Lucas-Kanade) at all pixels simultaneously search only over epipolar lines (many fewer candidate positions) slide: R. Szeliski

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Matching cost disparity LeftRight scanline Correspondence search Slide a window along the right scanline and compare contents of that window with the reference window in the left image Matching cost: SSD or normalized correlation slide: S. Lazebnik

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LeftRight scanline Correspondence search SSD slide: S. Lazebnik

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LeftRight scanline Correspondence search Norm. corr slide: S. Lazebnik

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Neighborhood size Smaller neighborhood: more details Larger neighborhood: fewer isolated mistakes w = 3w = 20 slide: R. Szeliski

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Failures of correspondence search Textureless surfaces Occlusions, repetition Non-Lambertian surfaces, specularities slide: S. Lazebnik

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How can we improve window-based matching? The similarity constraint is local (each reference window is matched independently) Need to enforce non-local correspondence constraints slide: S. Lazebnik

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Non-local constraints Uniqueness For any point in one image, there should be at most one matching point in the other image slide: S. Lazebnik

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Non-local constraints Uniqueness For any point in one image, there should be at most one matching point in the other image Ordering Corresponding points should be in the same order in both views slide: S. Lazebnik

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Non-local constraints Uniqueness For any point in one image, there should be at most one matching point in the other image Ordering Corresponding points should be in the same order in both views Ordering constraint doesn’t hold slide: S. Lazebnik

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Non-local constraints Uniqueness For any point in one image, there should be at most one matching point in the other image Ordering Corresponding points should be in the same order in both views Smoothness We expect disparity values to change slowly (for the most part) slide: S. Lazebnik

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I1I2I10 Multiple-baseline stereo results M. Okutomi and T. Kanade, “A Multiple-Baseline Stereo System,” IEEE Trans. on Pattern Analysis and Machine Intelligence, 15(4): (1993).“A Multiple-Baseline Stereo System,”

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Plane Sweep Stereo Choose a reference view Sweep family of planes at different depths with respect to the reference camera Each plane defines a homography warping each input image into the reference view reference camera input image R. Collins. A space-sweep approach to true multi-image matching. CVPR 1996.A space-sweep approach to true multi-image matching. input image

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Real-time 3D reconstruction from video “Real-Time Plane-sweeping Stereo with Multiple Sweeping Directions", CVPR D scene SAD as similarity (darker is higher similarity) warped images 46

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Real-time 3D reconstruction from video 47 “Real-Time Plane-sweeping Stereo with Multiple Sweeping Directions", CVPR D scene warped images SAD as similarity (darker is higher similarity)

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Real-time 3D reconstruction from video 48 “Real-Time Plane-sweeping Stereo with Multiple Sweeping Directions", CVPR D scene warped images SAD as similarity (darker is higher similarity)

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Real-time 3D reconstruction from video 49 “Real-Time Plane-sweeping Stereo with Multiple Sweeping Directions", CVPR D scene warped images SAD as similarity (darker is higher similarity) Multi-way sweep

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3D reconstruction from video view 1view N 50

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3D reconstruction from video 51

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