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 [matches1 matches2] = GenMatches(defaultPara, ImgInfo, FlagDisp) |
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40 | |
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41 | % This function generate Matches uing IMU and GPS info and Ransac and BA |
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42 | |
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43 | % 1. Mono calulation or load the pre-calculated data ------------------------ |
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44 | ImgInfo(1).appendOpt = 0; |
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45 | ImgInfo(2).appendOpt = 0; |
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46 | [ ImgInfo] = SingleModelInfo(defaultPara, ImgInfo); |
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47 | |
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48 | % initialize variables |
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49 | Img1 = strrep(ImgInfo(1).ExifInfo.name,'.jpg',''); |
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50 | Img2 = strrep(ImgInfo(2).ExifInfo.name,'.jpg',''); |
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51 | I1=imreadbw([defaultPara.Fdir '/pgm/' Img1 '.pgm']); % function from sift |
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52 | I2=imreadbw([defaultPara.Fdir '/pgm/' Img2 '.pgm']); % function from sift |
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53 | [f1] = readSurf(Img1, defaultPara.Fdir, 'Dense'); % original features |
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54 | [f2] = readSurf(Img2, defaultPara.Fdir, 'Dense'); % original features |
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55 | [D1 IND] = PorjPosi2Depth(size(I1), size(ImgInfo(1).Model.Depth.FitDepth), f1, ImgInfo(1).Model.Depth.FitDepth); |
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56 | [D2 IND] = PorjPosi2Depth(size(I2), size(ImgInfo(2).Model.Depth.FitDepth), f2, ImgInfo(1).Model.Depth.FitDepth); |
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57 | |
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58 | % 1. extract Measuesd Position and orientation from GPS or IMU info |
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59 | [MeasR MeasT] = InitPoseMeas(defaultPara, ImgInfo(1), ImgInfo(2)); |
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60 | |
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61 | % 2. Using Measures R and T and Mono-Depth to define mach search space constrain |
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62 | % read in all surf features |
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63 | [ Rc1, Rc2, ConS1, ConS2, ConSRough1, ConSRough2] = CalMatchSearchRegin(defaultPara, MeasR, MeasT, I1, I2, f1, f2, D1, D2, 1, FlagDisp); |
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64 | Vector2Ipoint([Rc1; ConS1],[defaultPara.Fdir '/surf/'],['RConS_' Img1]); |
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65 | Vector2Ipoint([Rc2; ConS2],[defaultPara.Fdir '/surf/'],['RConS_' Img2]); |
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66 | Vector2Ipoint([ConSRough1],[defaultPara.Fdir '/surf/'],['RConSRough_' Img1]); |
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67 | Vector2Ipoint([ConSRough2],[defaultPara.Fdir '/surf/'],['RConSRough_' Img2]); |
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68 | |
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69 | % 3. Do match search with all combinations satisfying Constrain from 2) using ralative threshould |
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70 | tic; |
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71 | cd match |
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72 | pwd |
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73 | % system(['./surfMatchRConS.sh ' defaultPara.Fdir ' ' Img1 ' ' Img2 ' _ 0.3 0.7']); |
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74 | system(['./surfMatchRConS.sh ' defaultPara.Fdir ' ' Img1 ' ' Img2 ' Dense ' '0.3 0.7']); % Parameter still need to be changed//Min |
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75 | cd .. |
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76 | toc |
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77 | |
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78 | % 4. Ransac |
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79 | [f1, f2, matches] = readSurfMatches(Img1, Img2, defaultPara.Fdir, [ defaultPara.Type 'Dense'], 1, 1); |
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80 | if isempty(matches) |
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81 | disp('Zeros matches'); |
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82 | matches1 = matches(1,:); |
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83 | matches2 = matches(2,:); |
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84 | return; |
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85 | end |
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86 | [D1 IND1] = PorjPosi2Depth(size(I1), size(ImgInfo(1).Model.Depth.FitDepth), f1(:,matches(1,:)), ImgInfo(1).Model.Depth.FitDepth); |
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87 | [D2 IND2] = PorjPosi2Depth(size(I2), size(ImgInfo(2).Model.Depth.FitDepth), f2(:,matches(2,:)), ImgInfo(1).Model.Depth.FitDepth); |
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88 | %figure(11); plotmatches(I1,I2,f1, f2,matches, 'Stacking','v','Interactive', FlagDisp); title('SurfMatch') |
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89 | %saveas(11,[defaultPara.ScratchFolder Img1 '_' Img2 'SimpleSurfMatch'],'jpg'); |
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90 | [F, inliers, NewDist, fail]=GeneralRansac(defaultPara, f1, f2, matches, D1, D2); |
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91 | figure(12); plotmatches(I1,I2,f1, f2,matches(:,inliers), 'Stacking', 'v', 'Interactive', FlagDisp); |
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92 | saveas(12,[defaultPara.ScratchFolder Img1 '_' Img2 'AfterRansac'],'jpg'); |
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93 | close 12; |
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94 | |
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95 | % *** Stop maunally to pick out the bad matches*** ----------------- |
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96 | matches = matches(:,inliers); |
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97 | if isempty(matches) |
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98 | disp('Zeros matches'); |
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99 | matches1 = matches(1,:); |
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100 | matches2 = matches(2,:); |
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101 | return; |
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102 | end |
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103 | |
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104 | % x_calib = [ inv(defaultPara.InrinsicK1)*[ f1(:,matches(1,:)); ones(1,length(matches))];... |
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105 | % inv(defaultPara.InrinsicK2)*[ f2(:,matches(2,:)); ones(1,length(matches))]]; |
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106 | % [ lamda1 lamda2] = triangulation( defaultPara, MeasR(1:3,:), MeasT(1:3), x_calib); |
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107 | % % end |
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108 | % X_obj_1 = x_calib(1:3,:).*repmat(lamda1, 3, 1); |
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109 | % X_obj_2 = MeasR(4:6,:)*(x_calib(4:6,:).*repmat(lamda2, 3, 1)) + repmat(MeasT(4:6), 1, length(matches)); |
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110 | % X_obj = (X_obj_1+X_obj_2)/2; |
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111 | % %end |
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112 | |
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113 | % 5. Bundle Adjustment |
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114 | % [R T X_obj_BA X_im_BA dist1_BA dist2_BA]=SparseBAWraper(defaultPara, MeasR, MeasT, [f1(:,matches(1,:)); f2(:,matches(2,:))], X_obj, ImgInfo, 1); |
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115 | % outlier_thre1 = prctile(dist1_BA,90); |
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116 | % outlier_thre2 = prctile(dist2_BA,90); |
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117 | % Outlier = dist1_BA > outlier_thre1 | dist2_BA > outlier_thre2; |
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118 | % lamda1(Outlier) = []; |
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119 | % lamda2(Outlier) = []; |
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120 | % X_obj_BA(:,Outlier) = []; |
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121 | % x_calib(:,Outlier) = []; |
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122 | % matches(:, Outlier) = []; |
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123 | % % [R T X_obj_BA X_im_BA dist1_BA dist2_BA]=SparseBAWraper(defaultPara, R, T, [f1(:,matches(1,:)); f2(:,matches(2,:))], X_obj_BA, ImgInfo, 1); |
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124 | % figure(13); plotmatches(I1,I2,f1, f2,matches, 'Stacking', 'v', 'Interactive', FlagDisp);title('after BA clean once'); |
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125 | % saveas(13,[defaultPara.ScratchFolder Img1 '_' Img2 'AfterBA'],'jpg'); |
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126 | |
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127 | matches1 = matches(1,:); |
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128 | matches2 = matches(2,:); |
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129 | return; |
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