[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 [model]=AddMatch2Model(defaultPara, Wrlname, lamda, Xim, ImgInfo, ImgScale, ImgIndex1, ImgIndex2, PostFixStr, Error) |
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| 40 | |
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| 41 | % This function load the previous mat file to get the model variables |
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| 42 | % Then add new match constrain in the model.ConstrainOccluMatch |
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| 43 | |
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| 44 | %load([defaultPar.Fdir '/data/' ]) % no need to load again ImgInfo is the latest version |
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| 45 | |
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| 46 | % ============================================================================ |
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| 47 | % need Sup Ray Depth |
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| 48 | % 1) Sup |
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| 49 | NumMatches = size(Xim,2); |
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| 50 | ScaleDepth = size(ImgInfo.Model.Depth.FitDepth); |
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| 51 | [IND1] = ProjPosi2Mask( ImgScale, ScaleDepth, Xim); |
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| 52 | SupMatched = ImgInfo.Model.Sup(IND1)'; % Sup |
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| 53 | mask = SupMatched == 0; % erase sky points |
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| 54 | SupMatched(mask)=[]; |
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| 55 | |
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| 56 | % 2) Ray |
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| 57 | x_calib = inv(defaultPara.InrinsicK1)*[ Xim; ones(1, NumMatches)]; |
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| 58 | RayNormfactor = sqrt( sum(x_calib.^2,1)); |
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| 59 | RayMatched = (x_calib./(repmat( RayNormfactor,3,1)))'; % Ray |
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| 60 | RayMatched(mask,:) = []; % erase sky points |
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| 61 | |
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| 62 | % depth |
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| 63 | % Notice: lamda is in the scale of local model already |
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| 64 | Depth_modified = lamda.*RayNormfactor; |
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| 65 | Depth_modified(:,mask) = []; % erase sky points |
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| 66 | % ========================================================================= |
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| 67 | |
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| 68 | % Compensate the number of the OccluMatches to the OriMatches ============= |
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| 69 | % count No of matches point to determine the weights |
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| 70 | NumMatches = size(ImgInfo.Model.Constrain.RayMatche,1); |
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| 71 | NumOccluMatches = size(RayMatched,1); |
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| 72 | % Ratio = ceil(NumMatches/NumOccluMatches); |
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| 73 | Gap = NumMatches - NumOccluMatches; |
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| 74 | |
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| 75 | if Gap > 0 |
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| 76 | if NumOccluMatches ~=1 |
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| 77 | distMap = ( sum((repmat(Xim, [1 1 NumOccluMatches]) - repmat( permute(Xim,[ 1 3 2]), [1 NumOccluMatches 1])).^2,1));% do not do the sqrt to put in some nonliearity |
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| 78 | distMap(distMap ==0) = Inf; |
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| 79 | dist = min( distMap,[],3); |
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| 80 | distAffectByError = dist./Error; |
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| 81 | TotalInvDistAffectByError = sum(1./distAffectByError); |
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| 82 | NumDuplicate = ceil( Gap*(1./distAffectByError./TotalInvDistAffectByError)); |
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| 83 | % NumDuplicate(1:NumOccluMatches) = 1; |
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| 84 | for i = 1:NumOccluMatches |
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| 85 | RayMatched = [RayMatched; repmat(RayMatched(i,:), NumDuplicate(i),1)]; |
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| 86 | Depth_modified = [ Depth_modified repmat(Depth_modified(:,i), 1, NumDuplicate(i))]; |
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| 87 | SupMatched = [SupMatched; repmat(SupMatched(i,:), NumDuplicate(i),1)]; |
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| 88 | end |
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| 89 | else |
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| 90 | NumDuplicate = Gap; |
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| 91 | RayMatched = [RayMatched; repmat(RayMatched, NumDuplicate,1)]; |
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| 92 | Depth_modified = [ Depth_modified repmat(Depth_modified, 1, NumDuplicate)]; |
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| 93 | SupMatched = [SupMatched; repmat(SupMatched, NumDuplicate,1)]; |
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| 94 | end |
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| 95 | end |
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| 96 | % ======================================================================= |
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| 97 | |
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| 98 | % add new match constrain |
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| 99 | ImgInfo.Model.ConstrainOccluMatch.RayMatche{ImgIndex2} = RayMatched; |
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| 100 | ImgInfo.Model.ConstrainOccluMatch.Depth_modified{ImgIndex2} = Depth_modified; |
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| 101 | ImgInfo.Model.ConstrainOccluMatch.SupMatched{ImgIndex2} = SupMatched; |
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| 102 | |
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| 103 | % new model variable |
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| 104 | model = ImgInfo.Model; |
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| 105 | if defaultPara.Flag.FlagRefinementDisp |
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| 106 | disp('Storaging of Occlusion Match Correcting Single Modle in model.ConstrainOccluMatch'); |
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| 107 | end |
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| 108 | save([defaultPara.Fdir '/data/' ImgInfo.ExifInfo.IDName '/' Wrlname '_' ImgInfo.ExifInfo.IDName '_' PostFixStr '.mat'],'model'); |
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| 109 | |
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| 110 | return; |
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