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 | Fdir ='/afs/cs/group/reconstruction3d/scratch/TestMultipleImage/ImgWithLaserData/' |
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40 | Img1 = 'img-manmade6-p-107t0' |
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41 | Img2 = 'img-manmade7-p-343t0' |
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42 | |
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43 | Default.fy = 2400.2091651084; |
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44 | Default.fx = 2407.3312729885838; |
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45 | Default.Ox = 1110.7122391785729;%2272/2; % |
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46 | Default.Oy = 833.72104535435108;%1704/2; % |
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47 | Default.a_default = 2272/Default.fx; %0.70783777; %0.129; % horizontal physical size of image plane normalized to focal length (in meter) |
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48 | Default.b_default = 1704/Default.fy; %0.946584169;%0.085; % vertical physical size of image plane normalized to focal length (in meter) |
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49 | Default.Ox_default = 1-Default.Ox/2272;%0.489272914; % camera origin offset from the image center in horizontal direction |
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50 | Default.Oy_default = 1-Default.Oy/1704;%0.488886982; % camera origin offset from the image center in vertical direction |
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51 | |
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52 | load([ Fdir '/data/depth_sph_corr-manmade6-p-107t0.mat']); |
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53 | LaserDepth1 = Position3DGrid(:,:,4); |
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54 | |
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55 | load([ Fdir '/data/depth_sph_corr-manmade7-p-343t0.mat']); |
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56 | LaserDepth2 = Position3DGrid(:,:,4); |
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57 | [Default.VertYNuDepth Default.HoriXNuDepth] = size( LaserDepth1); |
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58 | GoodMask = LaserDepth1 <80; |
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59 | |
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60 | load([ Fdir '/data/' Img1 '/Multi_TestLaserImg_img-manmade6-p-107t0_NonMono.mat' ]); |
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61 | TriDepth1 = model.Constrain.Depth_modified; |
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62 | [IND1] = ProjPosi2Mask( [2272 1704], [55 305], Pair.Xim([1 2],:)); |
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63 | [ReScale] = Rescale(TriDepth1, LaserDepth1(IND1)); |
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64 | TriDepth1 = TriDepth1*ReScale; |
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65 | MonoDepth1 = model.Depth.FitDepth; |
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66 | [ReScale] = Rescale(MonoDepth1, LaserDepth1); |
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67 | MonoDepth1 = ReScale*MonoDepth1; |
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68 | JointDepth1 = model.PlaneParaInfo.FitDepth; |
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69 | [ReScale] = Rescale(JointDepth1, LaserDepth1); |
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70 | JointDepth1 = ReScale*JointDepth1; |
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71 | |
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72 | [ Mrms Mabs_m Mrms_f Mabs_m_f] = errorMetric( MonoDepth1(GoodMask), LaserDepth1(GoodMask)) |
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73 | [ Jrms Jabs_m Jrms_f Jabs_m_f] = errorMetric( JointDepth1(GoodMask), LaserDepth1(GoodMask)) |
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74 | [ Trms Tabs_m Trms_f Tabs_m_f] = errorMetric( TriDepth1(:), LaserDepth1(IND1)') |
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75 | |
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76 | % [ rms abs_m rms_f abs_m_f] = errorMetric(D1, LaserDepth1) |
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77 | |
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78 | load([ Fdir '/data/' Img2 '/Multi_TestLaserImg_img-manmade7-p-343t0_NonMono.mat' ]); |
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79 | GoodMask = LaserDepth2 <80; |
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80 | TriDepth2 = model.Constrain.Depth_modified; |
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81 | [IND2] = ProjPosi2Mask( [2272 1704], [55 305], Pair.Xim([3 4],:)); |
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82 | [ReScale] = Rescale(TriDepth2, LaserDepth2(IND2)); |
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83 | TriDepth2 = TriDepth2*ReScale; |
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84 | |
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85 | MonoDepth2 = model.Depth.FitDepth; |
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86 | [ReScale] = Rescale(MonoDepth2, LaserDepth2); |
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87 | MonoDepth2 = ReScale*MonoDepth2; |
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88 | JointDepth2 = model.PlaneParaInfo.FitDepth; |
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89 | [ReScale] = Rescale( JointDepth2, LaserDepth2); |
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90 | JointDepth2 = ReScale*JointDepth2; |
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91 | |
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92 | [ Mrms Mabs_m Mrms_f Mabs_m_f] = errorMetric( MonoDepth2(GoodMask), LaserDepth2(GoodMask)) |
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93 | [ Jrms Jabs_m Jrms_f Jabs_m_f] = errorMetric( JointDepth2(GoodMask), LaserDepth2(GoodMask)) |
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94 | [ Trms Tabs_m Trms_f Tabs_m_f] = errorMetric( TriDepth2(:), LaserDepth2(IND2)') |
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95 | |
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96 | % rescale |
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97 | scaleMono1 = sdpvar(1,1); |
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98 | sol = solvesdp([],norm( scaleMono1*MonoDepth1(:) - LaserDepth1(:),1)); |
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