3D pose estimation

3D pose estimation is a process of predicting the transformation of an object from a user-defined reference pose, given an image or a 3D scan. It arises in computer vision or robotics where the pose or transformation of an object can be used for alignment of a computer-aided design models, identification, grasping, or manipulation of the object.

The image data from which the pose of an object is determined can be either a single image, a stereo image pair, or an image sequence where, typically, the camera is moving with a known velocity. The objects which are considered can be rather general, including a living being or body parts, e.g., a head or hands. The methods which are used for determining the pose of an object, however, are usually specific for a class of objects and cannot generally be expected to work well for other types of objects.

From an uncalibrated 2D camera

It is possible to estimate the 3D rotation and translation of a 3D object from a single 2D photo, if an approximate 3D model of the object is known and the corresponding points in the 2D image are known. A common technique developed in 1995 for solving this is POSIT, where the 3D pose is estimated directly from the 3D model points and the 2D image points, and corrects the errors iteratively until a good estimate is found from a single image. Most implementations of POSIT only work on non-coplanar points (in other words, it won't work with flat objects or planes).

Another approach is to register a 3D CAD model over the photograph of a known object by optimizing a suitable distance measure with respect to the pose parameters.

The distance measure is computed between the object in the photograph and the 3D CAD model projection at a given pose.