Implementation of Quasi Dense Stereo algorithm.

modules/README.md

@@ -46,6 +46,8 @@ $ cmake -D OPENCV_EXTRA_MODULES_PATH=<opencv_contrib>/modules -D BUILD_opencv_<r
 
 - **plot**: Plotting -- The plot module allows you to easily plot data in 1D or 2D.
 
+- **qds**: Dense Stereo Correspondence -- Densification of stereo matches using Quasi Dense Stereo method.
+
 - **reg**: Image Registration -- Pixels based image registration for precise alignment. Follows the paper "Image Alignment and Stitching: A Tutorial", by Richard Szeliski.
 
 - **rgbd**: RGB-Depth Processing module -- Linemod 3D object recognition; Fast surface normals and 3D plane finding. 3D visual odometry. 3d reconstruction using KinectFusion.

modules/qds/CMakeLists.txt

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+
+set(the_description "Quasi Dense Stereo")
+ocv_define_module(qds opencv_core opencv_imgproc opencv_video opencv_imgcodecs opencv_tracking OPTIONAL opencv_video opencv_imgproc opencv_imgcodecs opencv_core WRAP python)

modules/qds/README.md

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+Quasi Dense Stereo
+======================
+
+**qds**
+
+Quasi Dense Stereo is method for performing dense stereo matching. This module contains a class that implements this process.
+The code uses pyramidal Lucas-Kanade with Shi-Tomasi features to get the initial seed correspondences.
+Then these seeds are propagated by using mentioned growing scheme.

modules/qds/doc/qds.bib

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+@InProceedings{Stoyanov2010,
+author="Stoyanov, Danail
+and Scarzanella, Marco Visentini
+and Pratt, Philip
+and Yang, Guang-Zhong",
+editor="Jiang, Tianzi
+and Navab, Nassir
+and Pluim, Josien P. W.
+and Viergever, Max A.",
+title="Real-Time Stereo Reconstruction in Robotically Assisted Minimally Invasive Surgery",
+booktitle="Medical Image Computing and Computer-Assisted Intervention (MICCAI 2010)",
+year="2010",
+publisher="Springer Berlin Heidelberg",
+address="Berlin, Heidelberg",
+pages="275--282",
+abstract="The recovery of 3D tissue structure and morphology during robotic assisted surgery is an important step towards accurate deployment of surgical guidance and control techniques in minimally invasive therapies. In this article, we present a novel stereo reconstruction algorithm that propagates disparity information around a set of candidate feature matches. This has the advantage of avoiding problems with specular highlights, occlusions from instruments and view dependent illumination bias. Furthermore, the algorithm can be used with any feature matching strategy allowing the propagation of depth in very disparate views. Validation is provided for a phantom model with known geometry and this data is available online in order to establish a structured validation scheme in the field. The practical value of the proposed method is further demonstrated by reconstructions on various in vivo images of robotic assisted procedures, which are also available to the community.",
+isbn="978-3-642-15705-9"
+}
+
+
+
+
+@article{Lhuillier2000,
+abstract = {A new robust dense matching algorithm is introduced. The algorithm$\backslash$nstarts from matching the most textured points, then a match propagation$\backslash$nalgorithm is developed with the best first strategy to dense matching.$\backslash$nNext, the matching map is regularised by using the local geometric$\backslash$nconstraints encoded by planar affine applications and by using the$\backslash$nglobal geometric constraint encoded by the fundamental matrix. Two most$\backslash$ndistinctive features are a match propagation strategy developed by$\backslash$nanalogy to region growing and a successive regularisation by local and$\backslash$nglobal geometric constraints. The algorithm is efficient, robust and can$\backslash$ncope with wide disparity. The algorithm is demonstrated on many real$\backslash$nimage pairs, and applications on image interpolation and a creation of$\backslash$nnovel views are also presented},
+author = {Lhuillier, Maxime and Quan, Long},
+doi = {10.1109/ICPR.2000.905620},
+file = {:home/dimitrisps/Desktop/ucl/papers/quasiDenseMatching.pdf:pdf},
+isbn = {0-7695-0750-6},
+issn = {10514651},
+journal = {Proceedings-International Conference on Pattern Recognition},
+number = {1},
+pages = {968--972},
+title = {{Robust dense matching using local and global geometric constraints}},
+volume = {15},
+year = {2000}
+}

modules/qds/include/opencv2/qds.hpp

@@ -0,0 +1,73 @@
+/*
+By downloading, copying, installing or using the software you agree to this license.
+If you do not agree to this license, do not download, install,
+copy or use the software.
+
+
+                          License Agreement
+               For Open Source Computer Vision Library
+                       (3-clause BSD License)
+
+Copyright (C) 2015-2018, OpenCV Foundation, all rights reserved.
+Third party copyrights are property of their respective owners.
+
+Redistribution and use in source and binary forms, with or without modification,
+are permitted provided that the following conditions are met:
+
+  * Redistributions of source code must retain the above copyright notice,
+    this list of conditions and the following disclaimer.
+
+  * Redistributions in binary form must reproduce the above copyright notice,
+    this list of conditions and the following disclaimer in the documentation
+    and/or other materials provided with the distribution.
+
+  * Neither the names of the copyright holders nor the names of the contributors
+    may be used to endorse or promote products derived from this software
+    without specific prior written permission.
+
+This software is provided by the copyright holders and contributors "as is" and
+any express or implied warranties, including, but not limited to, the implied
+warranties of merchantability and fitness for a particular purpose are disclaimed.
+In no event shall copyright holders or contributors be liable for any direct,
+indirect, incidental, special, exemplary, or consequential damages
+(including, but not limited to, procurement of substitute goods or services;
+loss of use, data, or profits; or business interruption) however caused
+and on any theory of liability, whether in contract, strict liability,
+or tort (including negligence or otherwise) arising in any way out of
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+ */
+
+
+
+
+
+#ifndef __OPENCV_QDS_H__
+#define __OPENCV_QDS_H__
+
+#include <opencv2/qds/quasiDenseStereo.hpp>
+
+/**
+ * @defgroup qds Quasi Dense Stereo
+ * This module contains the code to perform quasi dense stereo matching.
+ * The method initially starts with a sparse 3D reconstruction based on feature matching across a
+ * stereo image pair and subsequently propagates the structure into neighboring image regions.
+ * To obtain initial seed correspondences, the algorithm locates Shi and Tomashi features in the
+ * left image of the stereo pair and then tracks them using pyramidal Lucas-Kanade in the right image.
+ * To densify the sparse correspondences, the algorithm computes the zero-mean normalized
+ * cross-correlation (ZNCC) in small patches around every seed pair and uses it as a quality metric
+ * for each match. In this code, we introduce a custom structure to store the location and ZNCC value
+ * of correspondences called "Match". Seed Matches are stored in a priority queue sorted according to
+ * their ZNCC value, allowing for the best quality Match to be readily available. The algorithm pops
+ * Matches and uses them to extract new matches around them. This is done by considering a small
+ * neighboring area around each Seed and retrieving correspondences above a certain texture threshold
+ * that are not previously computed. New matches are stored in the seed priority queue and used as seeds.
+ * The propagation process ends when no additional matches can be retrieved.
+ *
+ *
+ * @sa This code represents the work presented in @cite Stoyanov2010.
+ * If this code is useful for your work please cite @cite Stoyanov2010.
+ *
+ * Also the original growing scheme idea is described in @cite Lhuillier2000
+ *
+*/
+#endif // __OPENCV_QDS_H__