[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 [theta]= GenThetaFromPsi(x1, x2, psi); |
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| 40 | |
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| 41 | % This function is called by Matches2ViewpointTransform |
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| 42 | % give matches point and psi samples |
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| 43 | % calcuated each theta for all matches and all samples |
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| 44 | |
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| 45 | NumSample = length(psi); |
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| 46 | NumMatches = length(x1); |
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| 47 | |
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| 48 | % first step rotation * x1 |
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| 49 | R11 = permute( cos(psi), [ 1 3 2]); |
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| 50 | R13 = permute( sin(psi), [ 1 3 2]); |
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| 51 | Newx1 =[ repmat( x1(1,:), [1 1 NumSample]).*repmat( R11, [1 NumMatches 1]) + repmat( R13, [1 NumMatches 1]);... |
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| 52 | repmat( x1(2,:), [1 1 NumSample]);... |
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| 53 | repmat( x1(1,:), [1 1 NumSample]).*repmat( -R13, [1 NumMatches 1]) + repmat( R11, [1 NumMatches 1])]; %(3 x NumMatches x NumSample) |
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| 54 | |
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| 55 | % Last step find theta for each entries of Newx1 |
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| 56 | Interm = (Newx1(1,:,:).*repmat( x2(2,:), [1 1 NumSample]) - Newx1(2,:,:).*repmat( x2(1,:), [1 1 NumSample]))./... |
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| 57 | (Newx1(3,:,:).*repmat( x2(2,:), [1 1 NumSample]) + Newx1(2,:,:)); |
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| 58 | |
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| 59 | theta = atan( permute( Interm, [ 2 3 1])); |
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| 60 | |
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