3D pose estimation

3D pose estimation is the problem of determining the transformation of an object in a 2D image which gives the 3D object. The need for 3D pose estimation arises from the limitations of feature based pose estimation. There exist environments where it is difficult to extract corners or edges from an image. To circumvent these issues, the object is dealt with as a whole through the use of free-form contours.

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 for solving this has recently been "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. Perspective projection or orthogonal projection is possible depending on the pose representation used. This approach is appropriate for applications where a 3D CAD model of a known object (or object category) is available.

Given a 2D image of an object, and the camera that is calibrated with respect to a world coordinate system, it is also possible to find the pose which gives the 3D object in its object coordinate system. This works as follows.

Starting with a 2D image, image points are extracted which correspond to corners in an image. The projection rays from the image points are reconstructed from the 2D points so that the 3D points, which must be incident with the reconstructed rays, can be determined.

The algorithm for determining pose estimation is based on the Iterative Closest Point algorithm. The main idea is to determine the correspondences between 2D image features and points on the 3D model curve.

...
Wikipedia