3-point RANSAC

3-point RANSAC is a variant of the RANSAC algorithm used for estimating the parameters of a model from noisy data. It is specifically designed for estimating a 3D pose (position and orientation) of an object from a set of 3D points.

The algorithm works by randomly selecting three points from the set of input points and computing the pose of the object that best fits those three points. This is done by solving a set of equations that relate the 3D positions of the points to the object's pose.

Once the pose is computed, the algorithm checks how many of the remaining points in the set lie within a certain distance of the object, based on the computed pose. If enough points are found, the pose is considered a good fit and is returned as the estimated pose of the object.

If not enough points are found, the algorithm repeats the process with a new set of randomly selected points. This process is repeated for a fixed number of iterations, and the pose that has the highest number of inliers is returned as the final estimate.

3-point RANSAC is a simple and efficient algorithm for estimating the pose of an object from noisy 3D data. It is widely used in computer vision and robotics applications.