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[37] | 1 | See: |
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| 2 | license.m, |
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| 3 | LearningCode/README |
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| 4 | |
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| 5 | Version 0.1 |
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| 6 | |
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| 7 | Running OneShot3dEfficent the First Time |
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| 8 | ---------------------------------------- |
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| 9 | |
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| 10 | -Add the line "addpath(genpath('../ec2/bin/mex'));" to LearningCode/InitialPath.m, if it is not already there |
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| 11 | -start matlab |
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| 12 | -cd to LearningCode |
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| 13 | -Run InitialPath |
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| 14 | -Run "OneShot3dEfficient("input_image_name.jpg", "output_dir") |
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| 15 | |
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| 16 | |
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| 17 | |
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| 18 | |
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| 19 | A few points to note: |
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| 20 | 1. The code will not run out of the box. Therefore, unless |
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| 21 | you have read the ICCV-3dRR paper, it would be hard to |
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| 22 | make it run. |
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| 23 | 2. This code may differe from the one used for experiments |
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| 24 | in ICCV-3dRR. |
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| 25 | 3. (For our recent submission for ICCV-3dRR + ICCV-vrml + NIPS |
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| 26 | workshop on grammar of vision work, to IEEE-PAMI, please email us.) |
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| 27 | 4. For understanding the optimization used for learning and inference |
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| 28 | in the paper (minimization of L1 norms), please first see: |
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| 29 | http://www.stanford.edu/class/ee364a/lectures.html |
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| 30 | (lecture 4 on convex optimization problems) |
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| 31 | before emailing us. |
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| 32 | |
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| 33 | |
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| 34 | |
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| 35 | Useful links: |
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| 36 | http://make3d.stanford.edu/publications |
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| 37 | http://make3d.stanford.edu/publications/code |
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| 38 | http://make3d.stanford.edu/publications/faq |
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| 39 | http://make3d.stanford.edu/publications/contact |
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