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 [ track]=TrackBuilding(Matches) |
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40 | |
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41 | % This function given the Matches between all pairs of images |
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42 | % generate the track without any inconsistent (delete the track with any inconsistency): |
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43 | % in that one image observes multiple features in the track |
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44 | |
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45 | % add image in order from img1, img2, to imgxxx |
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46 | % When add new image look for matches between added images |
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47 | % and maintaining the matches |
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48 | |
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49 | NumImages = size(Matches,1); |
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50 | track = sparse(0,NumImages); |
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51 | InConsistMatchesBin = cell(1,NumImages); |
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52 | NumTrack = size( track,1); |
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53 | for i = 1:NumImages |
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54 | if i ==1 % can build track with only one image |
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55 | continue; |
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56 | end |
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57 | for j = 1:i-1 |
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58 | % i |
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59 | % j |
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60 | % Skip if Matches(j,i).Index is empty |
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61 | if isempty(Matches(j,i).Index) |
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62 | continue; |
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63 | end |
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64 | |
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65 | % Clean the Matches using InConsistMatchesBin |
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66 | NumInconsistMatchesOfJ = size(InConsistMatchesBin{j},1); |
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67 | if NumInconsistMatchesOfJ ~=0 |
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68 | NumMatces = size(Matches(j,i).Index, 2); |
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69 | DeleteMark = sum(repmat( Matches(j,i).Index, NumInconsistMatchesOfJ, 1) == repmat(InConsistMatchesBin{j}, 1, NumMatces), 1) ~= 0; |
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70 | Matches(j,i).Index(DeleteMark) = []; |
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71 | Matches(i,j).Index(DeleteMark) = []; |
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72 | end |
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73 | |
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74 | NumMatces = size(Matches(j,i).Index, 2); |
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75 | % editing existing track |
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76 | if NumTrack ~=0 |
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77 | Ptr = (repmat(track(:,j), 1, NumMatces) == ... |
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78 | repmat( Matches(j,i).Index, NumTrack, 1)); |
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79 | Target = repmat( Matches(i,j).Index, NumTrack, 1); |
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80 | NewTarget = sparse(NumTrack, 1); |
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81 | NewTarget(sum(Ptr,2)~=0) = sum( Target(Ptr), 2); |
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82 | Target = NewTarget; |
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83 | Matches_for_new_track_I = setdiff(Matches(i,j).Index, Target'); |
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84 | % check consistency |
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85 | InConsistMark = (Target ~= track(:,i)) & (track(:,i) ~= 0); |
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86 | track(:,i) = Target; |
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87 | % maintina the InConsistMatchesBin |
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88 | if ~all(InConsistMark==0) |
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89 | InConsistMatchesBin{j} = union(InConsistMatchesBin{j}, track(InConsistMark,j)); |
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90 | InConsistMatchesBin{i} = track(InConsistMark,i); |
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91 | track(InConsistMark,:) = []; |
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92 | end |
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93 | else |
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94 | Matches_for_new_track_I = Matches(i,j).Index; |
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95 | end |
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96 | |
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97 | % add new track |
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98 | NumNewTrack = length(Matches_for_new_track_I); |
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99 | Matches_for_new_track_mark_I = sum( repmat( Matches(i,j).Index, NumNewTrack, 1) ==... |
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100 | repmat( Matches_for_new_track_I', 1, NumMatces), 1) ~=0; |
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101 | NewTrack = sparse(NumNewTrack, NumImages); |
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102 | NewTrack(:,j) = Matches(j,i).Index(Matches_for_new_track_mark_I)'; |
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103 | NewTrack(:,i) = Matches(i,j).Index(Matches_for_new_track_mark_I)'; |
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104 | track = [track; NewTrack]; |
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105 | |
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106 | % maintain Num of track |
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107 | NumTrack = size( track,1); |
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108 | end |
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109 | end |
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