[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 [Pair fail]=TestWholePostMatch(defaultPara, ImgInfo1, ImgInfo2, PriorPose) |
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| 40 | |
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| 41 | % This function load the Initial SurfMatches and the OccluSurfMatches |
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| 42 | % then, do RANSAC algorithm to prune the bad matches out |
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| 43 | % Calucluate New Camera Pose (R and T) |
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| 44 | |
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| 45 | % Default Parameters ----------------------------- |
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| 46 | fail = 0; |
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| 47 | % ------------------------------------------------ |
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| 48 | |
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| 49 | % Initialize Variables --------------------------- |
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| 50 | Img1 = ImgInfo1.ExifInfo.IDName; |
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| 51 | Img2 = ImgInfo2.ExifInfo.IDName; |
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| 52 | I1=imreadbw([defaultPara.Fdir '/pgm/' Img1 '.pgm']); % function from sift |
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| 53 | I2=imreadbw([defaultPara.Fdir '/pgm/' Img2 '.pgm']); % function from sift |
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| 54 | % ------------------------------------------------ |
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| 55 | |
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| 56 | % Combine Both Initial SurfMatches and the OccluSurfMatches ------------------------- |
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| 57 | % Also Assign Confidence of each Matches |
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| 58 | % (Used as RANSAC Sampling Distribution) |
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| 59 | % 0) load cleaned Fisrt set of matches |
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| 60 | %defaultPara.MaxUniqueRatio |
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| 61 | load([defaultPara.Fdir '/data/' Img1 '_' Img2 '_PoseMatch.mat']); |
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| 62 | defaultPara.MaxUniqueRatio = 100; |
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| 63 | % 1) load Initial SurfMatches |
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| 64 | [f1, f2, matches] = readSurfMatches(Img1, Img2, defaultPara.Fdir, ... |
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| 65 | [ defaultPara.Type 'Dense_' num2str(defaultPara.AbsThre) '_' num2str(defaultPara.RatioThre)], 1, 1); |
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| 66 | if isempty(f1) |
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| 67 | [f1, f2, matches] = readSurfMatches(Img1, Img2, defaultPara.Fdir, ... |
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| 68 | ['Dense_' num2str(defaultPara.AbsThre) '_' num2str(defaultPara.RatioThre)], 1, 1); |
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| 69 | end |
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| 70 | |
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| 71 | % 2) load OccluSurfMatches |
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| 72 | [f1, f2, OccluedMatches] = readSurfMatches(Img1, Img2, defaultPara.Fdir, [ defaultPara.Type 'OccluDense'], 1, 1, 3);% need UniqueRatio//Min jobs |
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| 73 | UniqueRatio = OccluedMatches(3,:); |
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| 74 | %UniqueRatio = min(UniqueRatio,defaultPara.MaxUniqueRatio); |
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| 75 | Ptr = UniqueRatio > defaultPara.MaxUniqueRatio; |
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| 76 | UniqueRatio(Ptr) = Inf;%defaultPara.MaxUniqueRatio; |
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| 77 | |
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| 78 | OccluedMatches = OccluedMatches(1:2,:); |
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| 79 | if false |
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| 80 | disp('use CleanMatches'); |
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| 81 | matches = Matches; |
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| 82 | % removing initial matches that inconsistent with OccluedMatches |
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| 83 | [c i] = intersect( matches(1,:), OccluedMatches(1,:)); |
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| 84 | matches(:,i) = []; |
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| 85 | [c i] = intersect( matches(2,:), OccluedMatches(2,:)); |
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| 86 | matches(:,i) = []; |
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| 87 | matches = [matches OccluedMatches]; |
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| 88 | |
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| 89 | else |
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| 90 | disp('Number of Occlumatches used') |
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| 91 | size( OccluedMatches,2) |
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| 92 | |
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| 93 | % removing initial matches that inconsistent with OccluedMatches |
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| 94 | [c i] = intersect( matches(1,:), OccluedMatches(1,:)); |
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| 95 | matches(:,i) = []; |
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| 96 | [c i] = intersect( matches(2,:), OccluedMatches(2,:)); |
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| 97 | matches(:,i) = []; |
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| 98 | |
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| 99 | NumInitialMatches = size(matches, 2); |
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| 100 | matches = [matches OccluedMatches]; |
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| 101 | if isempty(matches) |
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| 102 | disp('Zeros Surf matches'); |
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| 103 | fail = 1; |
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| 104 | return; |
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| 105 | end |
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| 106 | |
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| 107 | % 3) Construct Prior Dist for All matches |
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| 108 | NMatches = size(matches, 2); |
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| 109 | PriorDist = ones(1, NMatches); |
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| 110 | PriorDist( (NumInitialMatches+1):end) = UniqueRatio; |
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| 111 | % 4) RANSAC |
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| 112 | disp('Number of matches used') |
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| 113 | |
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| 114 | if defaultPara.Flag.StorageDataBeforeRansac |
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| 115 | disp('Storage Data Before Ransac'); |
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| 116 | save([defaultPara.Fdir '/data/DataBeforeRansac.mat']); |
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| 117 | % return; |
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| 118 | end |
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| 119 | |
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| 120 | % Ensemble method to determine confidence of inliers |
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| 121 | fittingfn = @fundmatrix; |
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| 122 | distfnEnsmble = @fundistEnsmble; |
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| 123 | degenfn = @isdegenerate; |
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| 124 | x = [[f1(:, matches(1,:)); ones(1, NMatches)]; [f2(:, matches(2,:)); ones(1, NMatches)]]; |
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| 125 | [ SampsonDist ] = EnsembleRansac(defaultPara, x, fittingfn, distfnEnsmble, degenfn, 8, PriorDist', min(NMatches*10, defaultPara.MAXEnsembleSamples), 0); |
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| 126 | kurtosisValue =kurtosis(SampsonDist'); |
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| 127 | |
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| 128 | [F0, inliers, NewDist, fail, ind]=GeneralRansac(defaultPara, f1, f2, matches, [], [], kurtosisValue', 8); |
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| 129 | figure(100); plotmatches(I1,I2,f1, f2,matches(:,inliers), 'Stacking', 'h', 'Interactive', 3); |
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| 130 | matches = matches(:,inliers); % accept the pruning result |
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| 131 | if isempty(matches) |
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| 132 | disp('Zeros After Ransac matches'); |
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| 133 | fail = 2; |
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| 134 | return; |
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| 135 | end |
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| 136 | end |
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| 137 | % ------------------------------------------------------------------------------------ |
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| 138 | |
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| 139 | |
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| 140 | % Apply Bundle Adjustment Refinment Algorithm to Prune the Matches further ----------- |
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| 141 | % And Estimated the Pose |
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| 142 | % 1) initialize the 3D position of the matches given Prior Pose and Depths |
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| 143 | x_calib = [ inv(defaultPara.InrinsicK1)*[ f1(:,matches(1,:)); ones(1,size(matches,2))];... |
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| 144 | inv(defaultPara.InrinsicK2)*[ f2(:,matches(2,:)); ones(1,size(matches,2))]]; |
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| 145 | |
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| 146 | % Estimate F using NonLine LS on every inlier |
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| 147 | MatchDensityWeights1 = CalMatchDensityWeights(f1(:,matches(1,:)), max(size(I1))/defaultPara.radius2imageSizeRatio); |
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| 148 | MatchDensityWeights2 = CalMatchDensityWeights(f2(:,matches(2,:)), max(size(I2))/defaultPara.radius2imageSizeRatio); |
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| 149 | MatchDensityWeights =mean([MatchDensityWeights1; MatchDensityWeights2], 1); |
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| 150 | F = getFnpt( F0, f1(:, matches(1,:))', f2(:, matches(2,:))', MatchDensityWeights); |
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| 151 | E = defaultPara.InrinsicK2'*F*defaultPara.InrinsicK1; % Camera essential Matrix |
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| 152 | if ~isempty(PriorPose) |
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| 153 | [ R0, T0, lamda1, lamda2, inlier, Error] = EstPose( defaultPara, E, x_calib, [], PriorPose.R(1:3,:)); |
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| 154 | else |
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| 155 | [ R0, T0, lamda1, lamda2, inlier, Error] = EstPose( defaultPara, E, x_calib, [], []); |
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| 156 | end |
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| 157 | T0 = [T0; - R0'*T0]; |
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| 158 | R0 = [R0; R0']; |
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| 159 | matches = matches(:,inlier); % delet matches give negative depths |
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| 160 | x_calib = x_calib(:,inlier); |
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| 161 | lamda1 = lamda1(inlier); |
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| 162 | lamda2 = lamda2(inlier); |
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| 163 | |
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| 164 | X_obj_1 = x_calib(1:3,:).*repmat(lamda1, 3, 1); |
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| 165 | X_obj_2 = R0(4:6,:)*(x_calib(4:6,:).*repmat(lamda2, 3, 1)) + repmat(T0(4:6), 1, size(matches,2)); |
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| 166 | X_obj = (X_obj_1+X_obj_2)/2; |
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| 167 | |
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| 168 | % 2) |
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| 169 | [R T X_obj_BA X_im_BA dist1_BA dist2_BA]=SparseBAWraper(defaultPara, R0(1:3,:), T0(1:3), [f1(:,matches(1,:)); f2(:,matches(2,:))], X_obj, [ ImgInfo1 ImgInfo2], 1); |
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| 170 | if false % Min Modified for not pruning using BundleAdjustment |
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| 171 | if all(isnan( dist1_BA)) || isempty(R) || any(isnan(R(:))) |
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| 172 | disp('BA failed'); |
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| 173 | fail = 3; |
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| 174 | return; |
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| 175 | end |
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| 176 | while length(X_im_BA) >= defaultPara.MinimumNumMatches |
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| 177 | disp('BundleAdjClean') |
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| 178 | outlier_thre1 = prctile(dist1_BA,90); |
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| 179 | outlier_thre2 = prctile(dist2_BA,90); |
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| 180 | if outlier_thre1 >= defaultPara.ReProjErrorThre || outlier_thre2 >= defaultPara.ReProjErrorThre |
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| 181 | Outlier = dist1_BA > outlier_thre1 | dist2_BA > outlier_thre2; |
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| 182 | matches(:,Outlier) = []; |
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| 183 | if isempty(matches) |
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| 184 | disp('Zeros After BA matches'); |
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| 185 | fail = 4; |
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| 186 | return; |
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| 187 | end |
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| 188 | lamda1(Outlier) = []; |
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| 189 | lamda2(Outlier) = []; |
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| 190 | X_obj_BA(:,Outlier) = []; |
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| 191 | x_calib(:,Outlier) = []; |
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| 192 | [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, [ ImgInfo1 ImgInfo2], 1); |
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| 193 | if all(isnan( dist1_BA)) || isempty(R) || any(isnan(R(:))) |
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| 194 | disp('BA failed'); |
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| 195 | fail = 5; |
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| 196 | return; |
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| 197 | end |
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| 198 | else |
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| 199 | break; |
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| 200 | end |
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| 201 | end |
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| 202 | end |
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| 203 | % ------------------------------------------------------------------------------------ |
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| 204 | |
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| 205 | |
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| 206 | % Triangulation ---------------------------------------------------------------------- |
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| 207 | % modified the x_calib |
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| 208 | tempf1 = X_im_BA(1:2,:); |
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| 209 | tempf2 = X_im_BA(3:4,:); |
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| 210 | x_calib = [ inv(defaultPara.InrinsicK1)*[ tempf1; ones(1,length(tempf1))];... |
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| 211 | inv(defaultPara.InrinsicK2)*[ tempf2; ones(1,length(tempf2))]]; |
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| 212 | [ lamda1 lamda2 Error] = triangulation( defaultPara, R, T, x_calib); |
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| 213 | % Clean outlier triangulated depth |
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| 214 | %LamdaOutlier = lamda1 > 1000 | lamda1 <1; |
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| 215 | %matches(:,LamdaOutlier) = []; |
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| 216 | %if isempty(matches) |
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| 217 | % disp('Zeros After Tri matches'); |
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| 218 | % fail = 6; |
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| 219 | % return; |
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| 220 | %end |
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| 221 | |
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| 222 | %lamda1(LamdaOutlier) = []; |
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| 223 | %lamda2(LamdaOutlier) = []; |
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| 224 | %x_calib(:,LamdaOutlier) = []; |
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| 225 | |
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| 226 | % Pair Image Depth Scale |
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| 227 | [D1 IND1] = PorjPosi2Depth(size(I1), size(ImgInfo1.Model.Depth.FitDepth), f1(:,matches(1,:)), ImgInfo1.Model.Depth.FitDepth); |
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| 228 | [D2 IND2] = PorjPosi2Depth(size(I2), size(ImgInfo2.Model.Depth.FitDepth), f2(:,matches(2,:)), ImgInfo2.Model.Depth.FitDepth); |
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| 229 | Depth1ProjDepthRatio = sqrt(sum(x_calib(1:3,:).^2, 1)); |
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| 230 | Depth2ProjDepthRatio = sqrt(sum(x_calib(4:6,:).^2, 1)); |
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| 231 | DProj1 = D1./Depth1ProjDepthRatio; |
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| 232 | DProj2 = D2./Depth2ProjDepthRatio; |
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| 233 | [DepthScale1] = UniformDepthScale( defaultPara, DProj1, lamda1, ones(1,length(lamda1))); |
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| 234 | [DepthScale2] = UniformDepthScale( defaultPara, DProj2, lamda2, ones(1,length(lamda2)) ); |
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| 235 | %if DepthScale1 > 20 | DepthScale1 <0.05 | DepthScale2 > 20 | DepthScale2 <0.05 %//Min used to use 10 and 0.2 |
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| 236 | % disp('Unrealistic in Rescaleing the depth, Check matchings'); |
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| 237 | % fail = 6; |
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| 238 | % return; |
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| 239 | %end |
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| 240 | |
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| 241 | Pair.lamda = [lamda1; lamda2];%//Min add for debug |
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| 242 | Pair.DepthScale = [DepthScale1; DepthScale2]; |
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| 243 | Pair.R = R; |
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| 244 | Pair.T = T; |
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| 245 | Pair.Xim = [f1(:,matches(1,:)); f2(:,matches(2,:))]; |
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| 246 | % ----------------------------------------------------------------------------------- |
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