[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 [Sup, SupOri, SupNeighborTable]=CleanedSupNew(Default,Sup,maskSky, SupNeighborTable) |
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| 40 | |
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| 41 | displayFlag = false; |
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| 42 | ImCloseFlag = false; |
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| 43 | |
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| 44 | % get rid of the Sky for Sup |
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| 45 | SkyCandidate = unique(Sup(maskSky)); |
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| 46 | % testing sky candidate for two property |
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| 47 | % 1) is at least half of the superpixel are sky |
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| 48 | tempSkyCandidate = SkyCandidate; |
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| 49 | for i = tempSkyCandidate' |
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| 50 | mask = Sup == i; |
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| 51 | if sum(mask(maskSky))/sum(mask(:))<=0.5 % check if half is Sky |
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| 52 | SkyCandidate = setdiff(SkyCandidate', i)'; |
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| 53 | end |
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| 54 | end |
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| 55 | |
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| 56 | % 2) is at least one of its neighbor is sky |
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| 57 | tempSupNeighborTable = SupNeighborTable; |
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| 58 | for i = SkyCandidate' |
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| 59 | mask = Sup == i; |
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| 60 | Nei = setdiff(find(tempSupNeighborTable(i,:)),i); |
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| 61 | if sum( sum( repmat( SkyCandidate, 1, size(Nei,2)) == ... |
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| 62 | repmat( Nei, size(SkyCandidate,1), 1) )) >=1% check if at least one neighbor is sky |
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| 63 | Sup(mask) = 0; |
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| 64 | |
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| 65 | % clearn SupNeighborTable |
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| 66 | SupNeighborTable(i,:) = 0; |
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| 67 | SupNeighborTable(:,i) = 0; |
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| 68 | end |
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| 69 | end |
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| 70 | maskSky = Sup == 0; |
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| 71 | |
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| 72 | if displayFlag, |
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| 73 | DisplaySup(Sup, 2000); |
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| 74 | end |
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| 75 | |
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| 76 | SupOri = Sup; |
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| 77 | % extend the sky to merger small sup near sky |
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| 78 | ThreSmall = 10; |
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| 79 | SE = strel('disk',5); |
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| 80 | maskSky_dilate = imdilate(maskSky,SE); |
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| 81 | SmallSupNearSky = setdiff(Sup(maskSky_dilate),0); |
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| 82 | if ~isempty(SmallSupNearSky) |
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| 83 | SE = strel('disk',3); |
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| 84 | for i = SmallSupNearSky' |
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| 85 | mask = Sup ==i; |
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| 86 | if sum(mask(:)) < ThreSmall |
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| 87 | % naive merge |
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| 88 | mask_dilate = imdilate(mask,SE); |
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| 89 | mask_dilate(mask) = 0; |
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| 90 | mask_dilate(maskSky) = 0; |
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| 91 | target = mode(Sup(mask_dilate)); |
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| 92 | if isnan(target) |
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| 93 | Sup(mask) = 0; |
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| 94 | % clearn SupNeighborTable |
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| 95 | SupNeighborTable(i,:) = 0; |
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| 96 | SupNeighborTable(:,i) = 0; |
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| 97 | else |
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| 98 | Sup(mask) = target; |
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| 99 | SupNeighborTable( target,:) = ... |
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| 100 | SupNeighborTable( target,:) + SupNeighborTable( i,:); |
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| 101 | SupNeighborTable( :, target) = ... |
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| 102 | SupNeighborTable( :, target) + SupNeighborTable( :, i); |
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| 103 | % clearn SupNeighborTable |
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| 104 | SupNeighborTable(i,:) = 0; |
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| 105 | SupNeighborTable(:,i) = 0; |
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| 106 | end |
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| 107 | end |
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| 108 | end |
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| 109 | end |
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| 110 | |
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| 111 | if displayFlag, |
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| 112 | figure(250), imagesc(Sup), |
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| 113 | newmap = [rand(max(Sup(:)),3); [0 0 0]]; |
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| 114 | colormap(newmap); |
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| 115 | %DisplaySup(Sup, 3500); |
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| 116 | end |
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| 117 | |
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| 118 | |
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| 119 | if ImCloseFlag ==1 |
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| 120 | % 1) clearn the Sup first (default mask close) |
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| 121 | % with option to merge under other condition |
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| 122 | % use just a greedy closing algorithm |
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| 123 | NuSup = setdiff(unique(Sup)',0); |
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| 124 | se = strel('diamond',5); |
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| 125 | for i = NuSup |
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| 126 | mask = Sup == i; |
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| 127 | CloseMask = imclose(mask,se); |
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| 128 | ClosedCandidate = setdiff(unique(Sup(CloseMask)),i); |
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| 129 | if ~isempty(ClosedCandidate) |
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| 130 | for j = ClosedCandidate' |
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| 131 | if all(CloseMask(Sup == j)) |
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| 132 | Sup( Sup == j) = i; |
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| 133 | end |
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| 134 | end |
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| 135 | end |
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| 136 | end |
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| 137 | end |
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