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 [lamda1, lamda2] = Triangulate( R, T, X) |
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40 | |
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41 | % This function triangulate the depths (lamda) |
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42 | % given camera pose (R T: unit length) of a pair of images |
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43 | % Input |
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44 | % R - rotation |
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45 | % T - unit length translation vector |
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46 | % x - calibrated matches point in both images |
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47 | % Return |
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48 | % lamda - triangulated depth for unit length T |
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49 | |
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50 | X1 = X(1:3,:); |
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51 | X2 = X(4:6,:); |
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52 | T = T./(norm(T)); |
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53 | NumMatches = size(X1,2); |
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54 | M = sparse(0,0); |
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55 | LastM = []; |
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56 | X2M = sparse(0,0); |
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57 | for i = 1:NumMatches |
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58 | X2_hat = [[ 0 -X2(3,i) X2(2,i)];... |
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59 | [ X2(3,i) 0 -X2(1,i)];... |
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60 | [ -X2(2,i) X2(1,i) 0]]; |
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61 | LastM = [LastM; X2_hat*T]; |
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62 | M = blkdiag(M, X2_hat*R*X1(:,i)); |
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63 | X2M = blkdiag(X2M, X2(:,i)); |
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64 | end |
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65 | M = [M LastM]; |
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66 | [U S V] =svds(M); |
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67 | lamda = V(:,end); |
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68 | lamda = lamda./(lamda(end)); |
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69 | lamda1 = lamda(1:(end-1)); |
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70 | lamda2 = X2M\reshape( R*X1.*repmat(lamda1',3,1)+ repmat(T,1,int32(NumMatches)),[],1); |
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71 | |
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72 | return;edit Triangulate[ |
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