1 | % * This code was used in the following articles:
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2 | % * [1] Learning 3-D Scene Structure from a Single Still Image,
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3 | % * Ashutosh Saxena, Min Sun, Andrew Y. Ng,
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4 | % * In ICCV workshop on 3D Representation for Recognition (3dRR-07), 2007.
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5 | % * (best paper)
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6 | % * [2] 3-D Reconstruction from Sparse Views using Monocular Vision,
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7 | % * Ashutosh Saxena, Min Sun, Andrew Y. Ng,
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8 | % * In ICCV workshop on Virtual Representations and Modeling
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9 | % * of Large-scale environments (VRML), 2007.
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10 | % * [3] 3-D Depth Reconstruction from a Single Still Image,
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11 | % * Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng.
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12 | % * International Journal of Computer Vision (IJCV), Aug 2007.
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13 | % * [6] Learning Depth from Single Monocular Images,
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14 | % * Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng.
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15 | % * In Neural Information Processing Systems (NIPS) 18, 2005.
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16 | % *
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17 | % * These articles are available at:
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18 | % * http://make3d.stanford.edu/publications
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19 | % *
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20 | % * We request that you cite the papers [1], [3] and [6] in any of
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21 | % * your reports that uses this code.
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22 | % * Further, if you use the code in image3dstiching/ (multiple image version),
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23 | % * then please cite [2].
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24 | % *
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25 | % * If you use the code in third_party/, then PLEASE CITE and follow the
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26 | % * LICENSE OF THE CORRESPONDING THIRD PARTY CODE.
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27 | % *
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28 | % * Finally, this code is for non-commercial use only. For further
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29 | % * information and to obtain a copy of the license, see
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30 | % *
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31 | % * http://make3d.stanford.edu/publications/code
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32 | % *
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33 | % * Also, the software distributed under the License is distributed on an
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34 | % * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
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35 | % * express or implied. See the License for the specific language governing
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36 | % * permissions and limitations under the License.
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37 | % *
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38 | % */
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39 | function []=Matches2ViewpointTransform(defaultPara, x1, x2, theta_hat, psi_hat, FlagDisp) |
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40 | |
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41 | % This function generate the possible viewpoints of each matches |
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42 | % after collecting all the possible viewpoints for all the matches |
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43 | % we can count the most frequent viewpoints as the estimated viewpoints |
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44 | |
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45 | % hope this approach will be robust to outlier and |
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46 | % the initial search Measured viewpoint theta_hat, psi_hat |
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47 | % will make this approach better than (ransac then EstPose) |
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48 | |
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49 | % restriction: |
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50 | % we assume 1) only one axis of rotation |
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51 | % 2) there is no height translation |
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52 | |
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53 | % Input: |
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54 | % 1) measured translation angel [cos(theta_hat) 0 sin(theta_hat)] = [x y z] ; y =0; |
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55 | % 2) measured rotation angle [ [ cos(psi_hat) 0 sin(psi_hat) ];... |
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56 | % [ 0 1 0 ];... |
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57 | % [ -sin(psi_hat) 0 cos(psi_hat) ]]; |
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58 | |
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59 | % Output: |
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60 | % ViewPoint : |
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61 | % [ psi .......;... |
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62 | % theta .....;...] |
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63 | % the third axis is number of matches |
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64 | |
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65 | % default paramter |
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66 | psi_range = 30/180*pi; |
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67 | psi_step = 0.1/180*pi; |
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68 | NumMatches = size(x1,2); |
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69 | |
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70 | % initialize variable |
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71 | psi = (psi_hat -psi_range):psi_step:(psi_hat + psi_range); |
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72 | NumSample = size(psi,2); |
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73 | |
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74 | % calculate corresponding theta for each psi |
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75 | theta = GenThetaFromPsi(x1, x2, psi); |
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76 | |
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77 | %ViewPoint = cat(3, psi, theta); |
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78 | |
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79 | % plot the 2D sample intensitiy figure |
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80 | if FlagDisp |
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81 | figure; |
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82 | hold on; |
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83 | scatter(reshape(repmat(psi,NumMatches,1), [], 1), theta(:), 3); |
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84 | end |
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