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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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