[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 [Predicted]=gen_predicted(Default, f, FeatureSuperpixel, Select) |
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| 40 | %learningType,logScale,SkyExclude,LearnAlg,AbsFeaType,AbsFeaDate,WeiBatchNumber,logScale,SkyExclude,LearnNear) |
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| 41 | % this function generate the learned depth |
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| 42 | |
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| 43 | |
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| 44 | FeaVectorPics = genFeaVectorNew(Default, f,FeatureSuperpixel,... |
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| 45 | [1:Default.VertYNuDepth],[1:Default.HoriXNuDepth],1,0); |
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| 46 | |
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| 47 | for Option = Select |
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| 48 | |
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| 49 | if Option == 1 |
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| 50 | % load all the thiRow in different rows |
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| 51 | load([Default.ParaFolder 'Depth.mat']); |
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| 52 | Predicted.depthMap = exp(reshape(sum([ones(size(FeaVectorPics,2),1) FeaVectorPics'].*... |
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| 53 | repmat(thiRow',[Default.HoriXNuDepth 1]),2),Default.VertYNuDepth,[])); |
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| 54 | |
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| 55 | elseif Option == 2 |
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| 56 | % load all the VarRow in different rows |
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| 57 | load([Default.ParaFolder 'Var.mat']); |
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| 58 | Predicted.VarMap = exp(reshape(sum([ones(size(FeaVectorPics,2),1) FeaVectorPics'].*... |
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| 59 | repmat(VarRow',[Default.HoriXNuDepth 1]),2),Default.VertYNuDepth,[])); |
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| 60 | |
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| 61 | else |
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| 62 | % load all the GSRow in different rows |
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| 63 | load([Default.ParaFolder 'Ground.mat']); |
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| 64 | load([Default.ParaFolder 'Sky.mat']); |
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| 65 | Predicted.Ground = exp(reshape(sum([ones(size(FeaVectorPics,2),1) FeaVectorPics'].*... |
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| 66 | repmat( GroundRow',[Default.HoriXNuDepth 1]),2),Default.VertYNuDepth,[])); |
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| 67 | Predicted.Sky = exp(reshape(sum([ones(size(FeaVectorPics,2),1) FeaVectorPics'].*... |
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| 68 | repmat( GroundRow',[Default.HoriXNuDepth 1]),2),Default.VertYNuDepth,[])); |
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| 69 | Predicted.Ground = (1./(1+exp(-Predicted.Ground)))>0.5; |
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| 70 | Predicted.Sky = (1./(1+exp(-Predicted.Sky)))>0.5; |
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| 71 | |
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| 72 | end |
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| 73 | |
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| 74 | end |
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| 75 | return; |
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