[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]=DenseMatch(defaultPara, R, T, ImgInfo) |
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| 40 | |
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| 41 | % This function search for denser mach given pretty accurate R T |
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| 42 | |
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| 43 | Img1 = strrep(ImgInfo(1).ExifInfo.name,'.jpg',''); |
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| 44 | Img2 = strrep(ImgInfo(2).ExifInfo.name,'.jpg',''); |
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| 45 | I1=imreadbw([defaultPara.Fdir '/pgm/' Img1 '.pgm']); % function from sift |
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| 46 | I2=imreadbw([defaultPara.Fdir '/pgm/' Img2 '.pgm']); % function from sift |
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| 47 | [f1] = readSurf(Img1, defaultPara.Fdir, 'Dense'); % original features |
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| 48 | [f2] = readSurf(Img2, defaultPara.Fdir, 'Dense'); % original features |
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| 49 | [D1] = PorjPosi2Depth(size(I1), size(ImgInfo(1).Model.Depth.FitDepth), f1, ImgInfo(1).Model.Depth.FitDepth); |
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| 50 | [D2] = PorjPosi2Depth(size(I2), size(ImgInfo(2).Model.Depth.FitDepth), f2, ImgInfo(1).Model.Depth.FitDepth); |
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| 51 | |
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| 52 | % 1. Using BA's R and T and Scaled Mono-Depth to define match search space constrain |
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| 53 | % read in all surf features |
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| 54 | defaultPara.VertVar = 0.02; |
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| 55 | defaultPara.MaxRatio = 5; |
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| 56 | [ Rc1, Rc2, ConS1, ConS2, ConSRough1, ConSRough2] = CalMatchSearchRegin(defaultPara, [R; R'], [T; -R'*T], I1, I2, f1, f2, D1, D2, 1, 0); |
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| 57 | Vector2Ipoint([Rc1; ConS1],[defaultPara.Fdir '/surf/'],['RConS_' Img1]); |
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| 58 | Vector2Ipoint([Rc2; ConS2],[defaultPara.Fdir '/surf/'],['RConS_' Img2]); |
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| 59 | Vector2Ipoint([ConSRough1],[defaultPara.Fdir '/surf/'],['RConSRough_' Img1]); |
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| 60 | Vector2Ipoint([ConSRough2],[defaultPara.Fdir '/surf/'],['RConSRough_' Img2]); |
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| 61 | |
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| 62 | % 2. Do match search with all combinations satisfying Constrain from 2) using ralative threshould |
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| 63 | tic |
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| 64 | cd match |
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| 65 | system(['./surfMatchRConS.sh ' defaultPara.Fdir ' ' Img1 ' ' Img2 ' Dense ' '0.1 0.3']); |
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| 66 | cd .. |
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| 67 | toc |
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| 68 | |
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| 69 | [f1, f2, matches] = readSurfMatches(Img1, Img2, defaultPara.Fdir, [ defaultPara.Type 'Dense'], 1, 1); |
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| 70 | figure; plotmatches(I1,I2,f1, f2,matches, 'Stacking', 'v', 'Interactive', 2); |
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| 71 | f1 = f1(:,matches(1,:)); |
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| 72 | f2 = f2(:,matches(2,:)); |
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| 73 | |
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| 74 | % % 3. triangulation |
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| 75 | % x_calib = [ inv(defaultPara.InrinsicK1)*[ f1; ones(1,length(f1))];... |
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| 76 | % inv(defaultPara.InrinsicK2)*[ f2; ones(1,length(f2))]]; |
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| 77 | % [ Pair.depth1 Pair.depth2] = triangulation( defaultPara, R, T, x_calib); |
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| 78 | % X_obj_1 = x_calib(1:3,:).*repmat(Pair.depth1, 3, 1); |
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| 79 | % X_obj_2 = R'*(x_calib(4:6,:).*repmat(Pair.depth2, 3, 1)) + repmat(-R'*T, 1, length(f1)); |
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| 80 | % Structure.X_obj = (X_obj_1+X_obj_2)/2; |
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| 81 | Pair.Xim = [f1; f2]; |
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| 82 | Pair.R = R; |
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| 83 | Pair.T = T; |
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| 84 | return; |
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