3D single-object recognition

In computer vision, 3D single-object recognition involves recognizing and determining the pose of user-chosen 3D object in a photograph or range scan. Typically, an example of the object to be recognized is presented to a vision system in a controlled environment, and then for an arbitrary input such as a video stream, the system locates the previously presented object. This can be done either off-line, or in real-time. The algorithms for solving this problem are specialized for locating a single pre-identified object, and can be contrasted with algorithms which operate on general classes of objects, such as face recognition systems or 3D generic object recognition. Due to the low cost and ease of acquiring photographs, a significant amount of research has been devoted to 3D object recognition in photographs. In computer vision, 3D single-object recognition involves recognizing and determining the pose of user-chosen 3D object in a photograph or range scan. Typically, an example of the object to be recognized is presented to a vision system in a controlled environment, and then for an arbitrary input such as a video stream, the system locates the previously presented object. This can be done either off-line, or in real-time. The algorithms for solving this problem are specialized for locating a single pre-identified object, and can be contrasted with algorithms which operate on general classes of objects, such as face recognition systems or 3D generic object recognition. Due to the low cost and ease of acquiring photographs, a significant amount of research has been devoted to 3D object recognition in photographs. The method of recognizing a 3D object depends on the properties of an object. For simplicity, many existing algorithms have focused on recognizing rigid objects consisting of a single part, that is, objects whose spatial transformation is a Euclidean motion. Two general approaches have been taken to the problem: pattern recognition approaches use low-level image appearance information to locate an object, while feature-based geometric approaches construct a model for the object to be recognized, and match the model against the photograph.