Merge pull request #29224 from asmorkalov:as/ptcloud2

Dedicated pointcloud module #29224

OpenCV contrib: https://github.com/opencv/opencv_contrib/pull/4134

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake

doc/tutorials/ptcloud/point_cloud/point_cloud.markdown

@@ -0,0 +1,75 @@
+Point cloud visualisation {#tutorial_point_cloud}
+==============================
+
+|    |    |
+| -: | :- |
+| Original author | Dmitrii Klepikov |
+| Compatibility | OpenCV >= 5.0 |
+
+Goal
+----
+
+In this tutorial you will:
+
+-   Load and save point cloud data
+-   Visualise your data
+
+Requirements
+------------
+
+For visualisations you need to compile OpenCV library with OpenGL support.
+For this you should set WITH_OPENGL flag ON in CMake while building OpenCV from source.
+
+Practice
+-------
+
+Loading and saving of point cloud can be done using `cv::loadPointCloud` and `cv::savePointCloud` accordingly.
+
+Currently supported formats are:
+- [.OBJ](https://en.wikipedia.org/wiki/Wavefront_.obj_file) (supported keys are v(which is responsible for point position), vn(normal coordinates) and f(faces of a mesh), other keys are ignored)
+- [.PLY](https://en.wikipedia.org/wiki/PLY_(file_format)) (all encoding types(ascii and byte) are supported with limitation to only float type for data)
+
+@code{.py}
+vertices, normals = cv2.loadPointCloud("teapot.obj")
+@endcode
+
+Function `cv::loadPointCloud` returns vector of points of float (`cv::Point3f`) and vector of their normals(if specified in source file).
+To visualize it you can use functions from viz3d module and it is needed to reinterpret data into another format
+
+@code{.py}
+vertices = np.squeeze(vertices, axis=1)
+
+color = [1.0, 1.0, 0.0]
+colors = np.tile(color, (vertices.shape[0], 1))
+obj_pts = np.concatenate((vertices, colors), axis=1).astype(np.float32)
+
+cv2.viz3d.showPoints("Window", "Points", obj_pts)
+
+cv2.waitKey(0)
+@endcode
+
+In presented code sample we add a colour attribute to every point
+Result will be:
+
+![](tutorial_point_cloud_teapot.jpg)
+
+For additional info grid can be added
+
+@code{.py}
+vertices, normals = cv2.loadPointCloud("teapot.obj")
+@endcode
+
+![](teapot_grid.jpg)
+
+Other possible way to draw 3d objects can be a mesh.
+For that we use special functions to load mesh data and display it.
+Here for now only .OBJ files are supported and they should be triangulated before processing (triangulation - process of breaking faces into triangles).
+
+@code{.py}
+vertices, indices = cv2.loadMesh("../data/teapot.obj")
+vertices = np.squeeze(vertices, axis=1)
+
+cv2.viz3d.showMesh("window", "mesh", vertices, indices)
+@endcode
+
+![](teapot_mesh.jpg)