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Clemens Rabe , Thomas Muller, Andreas Wedel, and Uwe Franke Daimler Research, Sindelfingen

Semi-Global Matching + Dense, Robust, and Accurate Motion Field Estimation from Stereo Image Sequences in Real-time. Clemens Rabe , Thomas Muller, Andreas Wedel, and Uwe Franke Daimler Research, Sindelfingen ECCV 2010 Jonghee Park GIST CV-Lab. Introduction. Dense6D

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Clemens Rabe , Thomas Muller, Andreas Wedel, and Uwe Franke Daimler Research, Sindelfingen

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  1. Semi-Global Matching + Dense, Robust, and Accurate Motion Field Estimation from Stereo Image Sequences in Real-time Clemens Rabe, Thomas Muller, Andreas Wedel, and UweFranke Daimler Research, Sindelfingen ECCV 2010 Jonghee Park GIST CV-Lab.

  2. Introduction • Dense6D • Estimate the motion field by fusing dense stereo and optical flow • Variational6D • Replace optical flow of Dense6D by variational scene flow • Three approach of motion field estimation • Model based approaches • Need of a large variety of models • Sparse feature tracking methods using multiple image frames • Dense scene flow computation from two consecutive frames • Smoothness of motion field between consecutive frames • Suggest the use of Kalman filters for every image pixel

  3. Introduction

  4. Two Frame Motion Field Estimation • Combination of Optical Flow and Stereo • Optical Flow Problem • In : n 번째 프레임 • u : optical flow • Regularization of ill posed problem • Minimize following energy function • n = 2 : suffer from blurring effects around flow edges and over weights outliers • n = 1 : improved results • Stereo matching : SGM (on FPGA) • Straight-forward differential approach leads to insufficient results, due to noisy depth measurements

  5. SGM (HeikoHirschmuller, CVPR05) • Aggregation of Costs • Dynamic Programming = Shortest path problem • P1, P2 : Penalty of depth smoothness constraint • SGM = Multiple Path DP (16 path) + WTA (Winner Takes All)

  6. Dense6D • Projection model • Pin-hole camera model • d : disparity • b : baseline

  7. Dense6D(Temporal Integration of the Motion Field) • Kalman Filter • Direct measure of 3d motion suffers heavily from the immanent measurement noise • State vector (3D position, 3D velocity) • System model (linear motion) • : inverse motion of the observer • : time between both frames • Measurement vector • and • Measurement model

  8. Variational6D(Variational Scene Flow) • Global variational approach • Disparity change is regularized together with the optical flow • Data term • : time 1, 2 에서 Left, Right 영상의 intensity • d1 : time 1 에서 disparity

  9. Variational6D • Measurement Vector • Projection Matrix

  10. Evaluation

  11. Evaluation • Resolution: 640*480 • Execute time • Optical flow: 24ms • Dense scene flow: 65ms • Kalman filters: 12ms • Dense3D: 25Hz • Variational6D: 10Hz

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