[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 [ F, inliers, fail]=RansacOnPairMatches(defaultPara, f1, f2, matches, I1, I2, Depth1, Depth2, disp) |
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| 40 | |
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| 41 | % This function Use the Ransac algorithm to generate models |
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| 42 | % with Fundamental matrix and inlier matches |
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| 43 | % that suit the matches most |
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| 44 | % Input: |
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| 45 | % defaultPara - useful default parameters (like, camera intrinsic |
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| 46 | % matrix) |
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| 47 | % f1 - x,y coordinates of all feature frames in image 1 |
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| 48 | % f2 - same for image 2 |
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| 49 | % matches - 2 by nummatches array specifying the initial set of |
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| 50 | % possible matches between f1 and f2 |
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| 51 | % I1/I2 - optional images to display |
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| 52 | % Depth1/2 - depth imformation to support more accurate ransac |
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| 53 | % disp - if true, display the matches found when done. |
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| 54 | % See Also: |
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| 55 | % ransacfitfundmatrix.m (in kovesi) |
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| 56 | |
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| 57 | x1 = []; |
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| 58 | x2 = []; |
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| 59 | |
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| 60 | nmatches = size(matches, 2); |
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| 61 | for i=1:nmatches |
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| 62 | x1(i, 1:2) = f1(1:2, matches(1, i)); |
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| 63 | x2(i, 1:2) = f2(1:2, matches(2, i)); |
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| 64 | end |
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| 65 | |
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| 66 | % Assemble homogeneous feature coordinates for fitting of the |
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| 67 | % fundamental matrix, note that [x,y] corresponds to [col, row] |
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| 68 | x1 = [x1'; ones(1, length(x1))]; %[m1(2,:); m1(1,:); ones(1,length(m1))]; |
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| 69 | x2 = [x2'; ones(1, length(x1))]; %[m2(2,:); m2(1,:); ones(1,length(m1))]; |
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| 70 | X = [inv(defaultPara.InrinsicK1)*x1;... |
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| 71 | inv(defaultPara.InrinsicK2)*x2 ]; |
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| 72 | t = .002; % Distance threshold for deciding outliers |
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| 73 | |
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| 74 | % Initialize distribution to uniform |
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| 75 | dist = ones(nmatches,1); % using uniform dist gives better result |
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| 76 | dist = dist./sum(dist); |
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| 77 | |
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| 78 | % First Step Ransac to define dist from learned Depth |
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| 79 | [F, inliers, NewDist, fail] = ransacfitfundmatrix(defaultPara, x1, x2, t, Depth1, Depth2, dist, 1, disp, 0); |
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| 80 | if disp |
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| 81 | % figure(2) ; clf ; |
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| 82 | % plotmatches(I1,I2,f1, f2,matches(:, inliers), 'Stacking', 'v', 'Interactive', 0) ; |
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| 83 | figure(2) ; clf ; |
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| 84 | plotmatches(I1,I2,x1(1:2,:), x2(1:2,:), [inliers; inliers], 'Stacking', 'v', 'Interactive', 3) ; |
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| 85 | end |
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| 86 | |
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| 87 | % Estimated Distribution from the F and Depth info |
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| 88 | % assume the know the camera intrinsic parameter of both camera |
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| 89 | [x1, x2, Depth1, Depth2, X, lamda1, lamda2] = RemoveOutlier(x1, x2, Depth1, Depth2, X, [], [], inliers); |
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| 90 | [R, T, lamda1, lamda2, inlierPosD] = EstPose( ... |
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| 91 | defaultPara.InrinsicK2'*F*defaultPara.InrinsicK1, X); |
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| 92 | if disp |
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| 93 | figure(3) ; clf ; |
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| 94 | plotmatches(I1,I2,x1(1:2,:), x2(1:2,:), repmat(setdiff(1:size(X,2), inlierPosD), 2, 1), 'Stacking', 'v', 'Interactive', 0) ; |
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| 95 | end |
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| 96 | % Calculate EstDepMatchDist |
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| 97 | [x1, x2, Depth1, Depth2, X, lamda1, lamda2] = RemoveOutlier(x1, x2, Depth1, Depth2, X, lamda1, lamda2, inlierPosD); |
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| 98 | [dist, inlierThreDist] = EstDepMatchDist(X, R, T, Depth1, Depth2, lamda1', lamda2', disp); |
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| 99 | if disp |
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| 100 | figure(4) ; clf ; |
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| 101 | plotmatches(I1,I2,x1(1:2,:), x2(1:2,:), repmat(setdiff(1:size(X,2),inlierThreDist),2,1), 'Stacking', 'v', 'Interactive', 3) ; |
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| 102 | end |
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| 103 | |
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| 104 | % Second Ransac with new Distribution |
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| 105 | [x1, x2, Depth1, Depth2, X, lamda1, lamda2] = RemoveOutlier(x1, x2, Depth1, Depth2, X, lamda1, lamda2, inlierThreDist); |
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| 106 | [F, inliers, NewDist, fail] = ransacfitfundmatrix(defaultPara, x1, x2, t, Depth1, Depth2, dist, 1, disp, 0); |
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| 107 | |
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| 108 | if disp |
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| 109 | figure(5) ; clf ; |
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| 110 | plotmatches(I1,I2,x1(1:2,:), x2(1:2,:), repmat(inliers,2,1), 'Stacking', 'v', 'Interactive', 3) ; |
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| 111 | % vgg_gui_F(I1, I2, F'); |
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| 112 | end |
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