[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 [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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