[37] | 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 [ Matches]=CorrMatchPointsGivenOcclusion( ... |
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| 40 | defaultPara, Pair, GlobalScale,... |
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| 41 | POriReprojM1, FieldOccluPix1,PointPix1,... |
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| 42 | POriReprojM2, FieldOccluPix2,PointPix2,... |
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| 43 | ImgInfo1, ImgInfo2, ImgScale1, ImgScale2, ... |
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| 44 | FlagEarlyStopCorrMatchPointsGivenOcclusion) |
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| 45 | |
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| 46 | % This function perform correlation matches given Occlusion info |
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| 47 | |
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| 48 | if nargin < 12 |
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| 49 | FlagEarlyStopCorrMatchPointsGivenOcclusion = 0; |
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| 50 | end |
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| 51 | PostFixStrAfter = 'CorrMatches'; |
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| 52 | Img1 = ImgInfo1.ExifInfo.IDName |
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| 53 | Img2 = ImgInfo2.ExifInfo.IDName |
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| 54 | I1=imreadbw([defaultPara.Fdir '/pgm/' Img1 '.pgm']); % function from sift |
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| 55 | I2=imreadbw([defaultPara.Fdir '/pgm/' Img2 '.pgm']); % function from sift |
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| 56 | |
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| 57 | depthratioMin = 0.01; |
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| 58 | depthratioMax = 100; |
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| 59 | |
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| 60 | if ~isempty(Pair.lamda) |
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| 61 | depthratio = Pair.lamda(1,:)./Pair.lamda(2,:); |
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| 62 | % Min add to remove outliers (should be already removed when doing PoseEst.m) |
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| 63 | Inliers = depthratio > depthratioMin & depthratio < depthratioMax; |
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| 64 | depthratio = depthratio(Inliers); |
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| 65 | if ~isempty(depthratio) |
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| 66 | maxRatio = max(depthratio); |
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| 67 | minRatio = min(depthratio); |
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| 68 | else |
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| 69 | maxRatio = depthratioMax; |
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| 70 | minRatio = depthratioMin; |
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| 71 | end |
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| 72 | else |
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| 73 | maxRatio = max([GlobalScale(1)/GlobalScale(2) GlobalScale(2)/GlobalScale(1)]); |
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| 74 | minRatio = 1/maxRatio; |
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| 75 | end |
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| 76 | |
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| 77 | [Matches1 CoeffM1 Inliers1]=CorrolationMatch( defaultPara, Pair, I1, I2, PointPix1, POriReprojM1, FieldOccluPix1, [minRatio maxRatio]); |
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| 78 | if defaultPara.Flag.FlagRefinementDisp |
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| 79 | disp('Start Reverse CorrolationMatches Hold on'); |
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| 80 | % whos |
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| 81 | end |
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| 82 | |
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| 83 | Pair2_1.R = Pair.R'; |
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| 84 | Pair2_1.T = -Pair.R'*Pair.T; |
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| 85 | [Matches2 CoeffM2 Inliers2]=CorrolationMatch( defaultPara, Pair2_1, I2, I1, PointPix2, POriReprojM2, FieldOccluPix2, [1/maxRatio 1/minRatio]); |
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| 86 | if defaultPara.Flag.FlagRefinementDisp |
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| 87 | disp('CorrolationMatch.m Finished'); |
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| 88 | end |
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| 89 | |
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| 90 | Matches1 = Matches1(:,Inliers1); |
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| 91 | Matches2 = Matches2(:,Inliers2); |
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| 92 | CoeffM1 = CoeffM1(:,Inliers1); |
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| 93 | CoeffM2 = CoeffM2(:,Inliers2); |
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| 94 | |
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| 95 | % Check if the Matches are not mutual discard the one with less Coeff(Cross-Corrolation value) =============== |
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| 96 | Matches = [ Matches1 [Matches2(3:4,:); Matches2(1:2,:)]]; |
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| 97 | CoeffM = [ CoeffM1 CoeffM2]; |
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| 98 | % Min used different algorithm than SurFeature Matches |
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| 99 | [Inliers] = CleanMatch(Matches, CoeffM(1,:)); % choose the matches with higher Coeff is the matches is not mutual |
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| 100 | [InliersReverse] = CleanMatch(Matches([3 4 1 2],Inliers), CoeffM(1,Inliers)); % choose the matches with higher Coeff is the matches is not mutual |
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| 101 | Inliers = Inliers(InliersReverse); |
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| 102 | Matches = Matches(:,Inliers); |
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| 103 | CoeffM = CoeffM(:,Inliers); |
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| 104 | |
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| 105 | if defaultPara.Flag.FlagRefinementDisp |
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| 106 | figure; plotmatches(I1,I2,Matches(1:2,:), Matches(3:4,:),repmat(1:size(Matches,2),2,1), 'Stacking','v','Interactive', 3); |
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| 107 | end |
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| 108 | |
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| 109 | % save([defaultPara.Fdir '/data/' Img1 '_' Img2 '_' PostFixStrAfter 'BeforeFiltering.mat'],'Matches','CoeffM'); |
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| 110 | |
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| 111 | % use Coeff as threshould to filter out error matches |
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| 112 | CoeffMask = CoeffM(1,:) > defaultPara.CoeffMThre; |
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| 113 | [inlier, Residual] = EpipoPrune(defaultPara, Pair, Matches, ImgScale1); |
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| 114 | EpipolarResidualMask = Residual < defaultPara.ResidualThre; |
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| 115 | CoeffRationMask = CoeffM(2,:)./CoeffM(1,:) < defaultPara.coeffratioThre; |
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| 116 | CoeffRationMask( CoeffM(1,:) == 0) = false;% CoeffM(1,:) == 0 means not satisfy the epipolar constrain |
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| 117 | Mark = CoeffMask & CoeffRationMask & EpipolarResidualMask; |
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| 118 | |
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| 119 | % save([defaultPara.Fdir '/data/' Img1 '_' Img2 '_' PostFixStrAfter '.mat'],'Matches','CoeffM','Mark'); |
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| 120 | |
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| 121 | if FlagEarlyStopCorrMatchPointsGivenOcclusion |
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| 122 | Matches = Matches(:,Mark); |
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| 123 | save([defaultPara.Fdir '/data/' Img1 '_' Img2 '_' PostFixStrAfter '.mat'],'Matches','CoeffM'); |
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| 124 | return; |
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| 125 | end |
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| 126 | % ======================================================================= |
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| 127 | if ~isempty(Matches(:,Mark)) |
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| 128 | tempf1 = Matches(1:2,Mark); |
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| 129 | tempf2 = Matches(3:4,Mark); |
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| 130 | x_calib = [ inv(defaultPara.InrinsicK1)*[ tempf1; ones(1,size(tempf1,2))];... |
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| 131 | inv(defaultPara.InrinsicK2)*[ tempf2; ones(1,size(tempf2,2))]]; |
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| 132 | [ lamda1 lamda2 Error] = triangulation( defaultPara, Pair.R, Pair.T, x_calib); |
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| 133 | % notice lamda re-scale to local model scale |
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| 134 | lamda1 = lamda1./GlobalScale(1); |
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| 135 | lamda2 = lamda2./GlobalScale(2); |
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| 136 | % Storage the match result for later ReInference |
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| 137 | AddMatch2Model(defaultPara, defaultPara.Wrlname, lamda1, Matches(1:2,Mark), ImgInfo1, ImgScale1, Img1Index, Img2Index, PostFixStrAfter, Error); |
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| 138 | AddMatch2Model(defaultPara, defaultPara.Wrlname, lamda2, Matches(3:4,Mark), ImgInfo2, ImgScale2, Img2Index, Img1Index, PostFixStrAfter, Error); |
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| 139 | % Storage the New Matches |
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| 140 | % if defaultPara.Flag.FlagRefinementDisp |
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| 141 | disp('Storaging Occlusion Surf Features Matches'); |
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| 142 | save([defaultPara.Fdir '/data/' Img1 '_' Img2 '_' PostFixStrAfter '.mat'],'Matches','CoeffM','Error','Mark'); |
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| 143 | % end |
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| 144 | end |
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| 145 | |
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| 146 | return; |
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