cv2.triangulatePoints is a function in OpenCV that triangulates a 2D point from two views. The function is part of the open source project, cv2, and is available through the OpenCV python bindings. The function is based on observations from a stereo camera. It is not recommended as a method for triangulation. The function is not very efficient. Rather, you should consider using the more robust function of cv3.triangulatePoints.
The function works by transforming two camera images into a single image and then triangulating the point of interest in the image. The function is also able to triangulate a point in the world frame. When the problem is solved in this way, the results are more accurate. Moreover, it also uses the correct coordinate system to represent 3D points. It is based on the assumption that all points in 3D space have finite coordinates.
The function uses a 5-point algorithm to determine the point of interest. It also supports pairwise triangulation, which yields more accurate results. It also supports stereo triangulation in a user-defined coordinate system. In addition, it supports the OpenCV SfM module, which is based on Libmv. This module contains algorithms for 3D reconstruction from 2D images. This module uses a light version of Libmv, which is based on the work of Sameer Agarwal and Keir Mierle.
The function also supports the cv2.getPerspectiveTransform method, which enumerates the features of two contiguous frames. The function returns the average NED coordinate for each match. The function also calculates the two translations between the frames. Using the function requires PIL and PIL3. However, the function does not support the more advanced methods such as libmv’s getRecursiveMatrix. The function is also capable of triangulating points in the GPS coordinate system.
The function also supports the opencv_sfm module, which is based on the work of sameer agarwal and keir miere. This module uses a light version of the libmv library to perform 3D reconstruction from 2D images. It also imports reconstruction files. The function also supports the cv3.findEssentialMat method, which uses the 5-point algorithm to determine the corresponding matrix. It also supports the opencv_sfm_import method, which enables the import of files containing the SfM module.
The function also supports the opencv_sfm_import_example method, which imports a file containing a single point in a single image. It then uses the functions of the SfM module to determine the 3D point of interest in the image. It then calculates the average NED coordinate for each match. Using the functions of the SfM module is a much more efficient method. It is also based on the assumption that all points in the image have finite coordinates.
The function also supports the cv3.triangulatePoints method, which is based on observations from a stereo image. The function is based on the assumption that all points on a line in 3D space are projected to a point in the image. It is based on the assumption that the corresponding lines do not always intersect with the point of interest.