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 [F, inliers, fail] = ransacmatches(defaultPara, f1, f2, matches, I1, I2, disp) |
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40 | % Computes the fundamental matrix and inlier matches using ransac. Points |
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41 | % are sampled non-uniformly in order to prefer more matches that are spread |
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42 | % across the image. Otherwise the algorithm is standard. |
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43 | % input: f1 - x,y coordinates of all feature frames in image 1 |
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44 | % f2 - same for image 2 |
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45 | % matches - 2 by nummatches array specifying the initial set of |
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46 | % possible matches between f1 and f2 |
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47 | % I1/I2 - optional images to display |
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48 | % disp - if true, display the matches found when done. |
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49 | |
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50 | x1 = []; |
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51 | x2 = []; |
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52 | |
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53 | nmatches = size(matches, 2) |
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54 | for i=1:nmatches |
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55 | x1(i, 1:2) = f1(1:2, matches(1, i)); |
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56 | x2(i, 1:2) = f2(1:2, matches(2, i)); |
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57 | end |
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58 | |
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59 | % calculate distances |
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60 | % d1 is the sum of the squares of the distance from each point |
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61 | % in im1 to every other point |
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62 | % will use dist (the normalized avg distance for that match) |
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63 | % to weight the sampling algorithm for ransac |
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64 | d1=[]; |
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65 | d2=[]; |
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66 | for i=1:nmatches |
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67 | d1(i) = sum(sum( ((ones(nmatches, 1) * x1(i, 1:2)) - x1).^2)); |
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68 | d2(i) = sum(sum( ((ones(nmatches, 1) * x2(i, 1:2)) - x2).^2)); |
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69 | end |
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70 | dist=(d1+d2)/sum(sum(d1+d2)); |
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71 | |
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72 | % Assemble homogeneous feature coordinates for fitting of the |
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73 | % fundamental matrix, note that [x,y] corresponds to [col, row] |
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74 | x1 = [x1'; ones(1, length(x1))]; %[m1(2,:); m1(1,:); ones(1,length(m1))]; |
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75 | x2 = [x2'; ones(1, length(x1))]; %[m2(2,:); m2(1,:); ones(1,length(m1))]; |
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76 | |
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77 | t = .002; % Distance threshold for deciding outliers |
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78 | t_more = .001; % Distance threshold for deciding outliers (MS added) |
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79 | |
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80 | [F, inliers, fail] = ransacfitfundmatrix(defaultPara, x1, x2, t, zeros(2), zeros(2), dist, 1, 1, 0); |
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81 | % [inliers_more] = ransacPrune(F, x1, x2, t_more, 1, dist); % MS added |
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82 | |
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83 | if disp |
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84 | figure(2) ; clf ; |
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85 | plotmatches(I1,I2,f1, f2,matches(:, inliers), 'Stacking', 'v'); %, 'Interactive', 1) ; |
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86 | vgg_gui_F(I1, I2, F'); |
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87 | end |
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