The most common simplification is to assume known calibration parameters which is the so-called Perspective-*n*-Point problem: There are, though, several simplifications to the problem which turn into an extensive list of different algorithms that improve the accuracy of the DLT. It could be established with a minimum of 6 correspondences, using the well known Direct Linear Transform (DLT) algorithm. The most general version of the problem requires estimating the six degrees of freedom of the pose and five calibration parameters: focal length, principal point, aspect ratio and skew. In computer vision estimate the camera pose from n 3D-to-2D point correspondences is a fundamental and well understood problem. Linear Kalman Filter for bad poses rejection. Match scene descriptors with model descriptors using Flann matcher.Extract ORB features and descriptors from the scene.Read 3D textured object model and object mesh.The application will have the following parts: In this tutorial is explained how to build a real time application to estimate the camera pose in order to track a textured object with six degrees of freedom given a 2D image and its 3D textured model. However, this is not a trivial problem to solve due to the fact that the most common issue in image processing is the computational cost of applying a lot of algorithms or mathematical operations for solving a problem which is basic and immediately for humans. The most elemental problem in augmented reality is the estimation of the camera pose respect of an object in the case of computer vision area to do later some 3D rendering or in the case of robotics obtain an object pose in order to grasp it and do some manipulation. Nowadays, augmented reality is one of the top research topic in computer vision and robotics fields. Next Tutorial: Interactive camera calibration application Prev Tutorial: Camera calibration With OpenCV
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