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期末 Demo 報告 Cross-based

期末 Demo 報告 Cross-based. 2012/08/13 指導教授：詹寶珠教授 報告者：王邦威. Outline. Flow chart Method Implement on GPU Experimental results. Introduction. left. right. Local base algorithm. L. R. y. P(Lx,Ly). P’( Rx,Ry ). Disparity= Lx-Rx. Flow chart. Support region construction. Matching cost.

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期末 Demo 報告 Cross-based

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  1. 期末Demo報告Cross-based 2012/08/13 指導教授：詹寶珠教授 報告者：王邦威 1

  2. Outline Flow chart Method Implement on GPU Experimental results 2

  3. Introduction left right 3

  4. Local base algorithm L R y P(Lx,Ly) P’(Rx,Ry) Disparity= Lx-Rx 4

  5. Flow chart Support region construction Matching cost Cost aggregation Winner-take-all Post-processing 5

  6. Cross-based local support region construction • Two constraints • L • d • 2 6

  7. 左圖 右圖 Locally adaptive matching cost aggregation R. Zabih and J. Woodfill, “Non-parametric local transforms for computing visual correspondence,” in Proc. ECCV, 1994, pp. 151–158. String 2 • Matching cost • CAD : • Ccensus : Hamming distance of the two strings that stand for p and pd 7

  8. Locally adaptive matching cost aggregation • Cost aggregation • d • Winner-take-all • f 8

  9. Left/right consistency check • We apply occlusion treatment via left/right consistency checking to check the condition . • Then we fill in the disparity for invalidated pixels. For an invalidated pixel , we search its closest valid pixel to the left and to the right. 9

  10. Implement on GPU Host Device CPP檔 (主程式且包含呼叫OpenCV程式片段) DLL檔 (包含呼叫GPU程式片段) GPU (kernel function) • 整合CUDA和Open CV • 建一個專案，將含有cuda的程式碼部分包成一個Dll檔 • 對主函式和含有Open CV的程式碼另外建一個專案，寫在Cpp檔 • 在利用到GPU就呼叫Dll檔 10

  11. Implement on GPU Width One block Height Number of threads : width Number of blocks : height 11

  12. Experimental results 384x288 執行時間:0.058秒 12

  13. Experimental results時間分析-AD • 原程式 • 計算matching cost和水平區域cost總和:0.625秒 • 長區域所花時間:2.67秒 • 計算垂直上每點的水平cost總合和WTA:0.561秒 • 後處理:0.017秒 • 總共:3.941秒 • 平行化的程式 • 計算matching cost和水平區域cost總和:0.022秒 • 長區域所花時間:0.012秒 • 計算垂直上每點的水平cost總合和WTA:0.022秒 • 後處理:0.002秒 • 總共:0.058秒 • 加速68倍 13

  14. Experimental results 384x288 執行時間:0.273秒 14

  15. Experimental results時間分析-AD&Census • 原程式 • 計算Census cost:1.646秒 • 加總水平區域的cost:0.406秒 • 長區域: 2.67秒 • 計算垂直上每點的水平cost總合和WTA:0.561秒 • 後處理:0.017秒 • 總共:5.684秒 • 平行化的程式 • 計算Census cost:0.21秒 • 加總水平區域的cost:0.026秒 • 長區域:0.012秒 • 計算垂直上每點的水平cost總合和WTA:0.025秒 • 後處理:0.002秒 • 總共:0.273秒 • 加速20倍 15

  16. Conclusions 對於擁有許多不相依性計算的方法，可以很容易達到不錯的加速效能 適當的利用share memory，可以達到更快的速度 16

  17. Thank for your attention! 17

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