1 | % * This code was used in the following articles:
|
---|
2 | % * [1] Learning 3-D Scene Structure from a Single Still Image,
|
---|
3 | % * Ashutosh Saxena, Min Sun, Andrew Y. Ng,
|
---|
4 | % * In ICCV workshop on 3D Representation for Recognition (3dRR-07), 2007.
|
---|
5 | % * (best paper)
|
---|
6 | % * [2] 3-D Reconstruction from Sparse Views using Monocular Vision,
|
---|
7 | % * Ashutosh Saxena, Min Sun, Andrew Y. Ng,
|
---|
8 | % * In ICCV workshop on Virtual Representations and Modeling
|
---|
9 | % * of Large-scale environments (VRML), 2007.
|
---|
10 | % * [3] 3-D Depth Reconstruction from a Single Still Image,
|
---|
11 | % * Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng.
|
---|
12 | % * International Journal of Computer Vision (IJCV), Aug 2007.
|
---|
13 | % * [6] Learning Depth from Single Monocular Images,
|
---|
14 | % * Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng.
|
---|
15 | % * In Neural Information Processing Systems (NIPS) 18, 2005.
|
---|
16 | % *
|
---|
17 | % * These articles are available at:
|
---|
18 | % * http://make3d.stanford.edu/publications
|
---|
19 | % *
|
---|
20 | % * We request that you cite the papers [1], [3] and [6] in any of
|
---|
21 | % * your reports that uses this code.
|
---|
22 | % * Further, if you use the code in image3dstiching/ (multiple image version),
|
---|
23 | % * then please cite [2].
|
---|
24 | % *
|
---|
25 | % * If you use the code in third_party/, then PLEASE CITE and follow the
|
---|
26 | % * LICENSE OF THE CORRESPONDING THIRD PARTY CODE.
|
---|
27 | % *
|
---|
28 | % * Finally, this code is for non-commercial use only. For further
|
---|
29 | % * information and to obtain a copy of the license, see
|
---|
30 | % *
|
---|
31 | % * http://make3d.stanford.edu/publications/code
|
---|
32 | % *
|
---|
33 | % * Also, the software distributed under the License is distributed on an
|
---|
34 | % * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
|
---|
35 | % * express or implied. See the License for the specific language governing
|
---|
36 | % * permissions and limitations under the License.
|
---|
37 | % *
|
---|
38 | % */
|
---|
39 | function [Pair]=DenseMatch(defaultPara, R, T, ImgInfo) |
---|
40 | |
---|
41 | % This function search for denser mach given pretty accurate R T |
---|
42 | |
---|
43 | Img1 = strrep(ImgInfo(1).ExifInfo.name,'.jpg',''); |
---|
44 | Img2 = strrep(ImgInfo(2).ExifInfo.name,'.jpg',''); |
---|
45 | I1=imreadbw([defaultPara.Fdir '/pgm/' Img1 '.pgm']); % function from sift |
---|
46 | I2=imreadbw([defaultPara.Fdir '/pgm/' Img2 '.pgm']); % function from sift |
---|
47 | [f1] = readSurf(Img1, defaultPara.Fdir, 'Dense'); % original features |
---|
48 | [f2] = readSurf(Img2, defaultPara.Fdir, 'Dense'); % original features |
---|
49 | [D1] = PorjPosi2Depth(size(I1), size(ImgInfo(1).Model.Depth.FitDepth), f1, ImgInfo(1).Model.Depth.FitDepth); |
---|
50 | [D2] = PorjPosi2Depth(size(I2), size(ImgInfo(2).Model.Depth.FitDepth), f2, ImgInfo(1).Model.Depth.FitDepth); |
---|
51 | |
---|
52 | % 1. Using BA's R and T and Scaled Mono-Depth to define match search space constrain |
---|
53 | % read in all surf features |
---|
54 | defaultPara.VertVar = 0.02; |
---|
55 | defaultPara.MaxRatio = 5; |
---|
56 | [ Rc1, Rc2, ConS1, ConS2, ConSRough1, ConSRough2] = CalMatchSearchRegin(defaultPara, [R; R'], [T; -R'*T], I1, I2, f1, f2, D1, D2, 1, 0); |
---|
57 | Vector2Ipoint([Rc1; ConS1],[defaultPara.Fdir '/surf/'],['RConS_' Img1]); |
---|
58 | Vector2Ipoint([Rc2; ConS2],[defaultPara.Fdir '/surf/'],['RConS_' Img2]); |
---|
59 | Vector2Ipoint([ConSRough1],[defaultPara.Fdir '/surf/'],['RConSRough_' Img1]); |
---|
60 | Vector2Ipoint([ConSRough2],[defaultPara.Fdir '/surf/'],['RConSRough_' Img2]); |
---|
61 | |
---|
62 | % 2. Do match search with all combinations satisfying Constrain from 2) using ralative threshould |
---|
63 | tic |
---|
64 | cd match |
---|
65 | system(['./surfMatchRConS.sh ' defaultPara.Fdir ' ' Img1 ' ' Img2 ' Dense ' '0.1 0.3']); |
---|
66 | cd .. |
---|
67 | toc |
---|
68 | |
---|
69 | [f1, f2, matches] = readSurfMatches(Img1, Img2, defaultPara.Fdir, [ defaultPara.Type 'Dense'], 1, 1); |
---|
70 | figure; plotmatches(I1,I2,f1, f2,matches, 'Stacking', 'v', 'Interactive', 2); |
---|
71 | f1 = f1(:,matches(1,:)); |
---|
72 | f2 = f2(:,matches(2,:)); |
---|
73 | |
---|
74 | % % 3. triangulation |
---|
75 | % x_calib = [ inv(defaultPara.InrinsicK1)*[ f1; ones(1,length(f1))];... |
---|
76 | % inv(defaultPara.InrinsicK2)*[ f2; ones(1,length(f2))]]; |
---|
77 | % [ Pair.depth1 Pair.depth2] = triangulation( defaultPara, R, T, x_calib); |
---|
78 | % X_obj_1 = x_calib(1:3,:).*repmat(Pair.depth1, 3, 1); |
---|
79 | % X_obj_2 = R'*(x_calib(4:6,:).*repmat(Pair.depth2, 3, 1)) + repmat(-R'*T, 1, length(f1)); |
---|
80 | % Structure.X_obj = (X_obj_1+X_obj_2)/2; |
---|
81 | Pair.Xim = [f1; f2]; |
---|
82 | Pair.R = R; |
---|
83 | Pair.T = T; |
---|
84 | return; |
---|