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Given two or more images of the same 3D scene, taken from different points of view, the correspondence problem is to find a set of points in one image which can be identified as the same points in another image. A human can normally solve this problem quickly and easily, even when the images contain significant amount of noise. In computer vision the correspondence problem is studied for the case when a computer should solve it automatically with only the images as input. Once the correspondence problem has been solved, resulting in a set of image points which are in correspondence, other methods can be applied to this set to reconstruct the position of the corresponding 3D points in the scene.

The correspondence problem typically occurs when two images of the same scene are used, the stereo correspondence problem. This concept can be generalized to the three-view correspondence problem or, in general, the N-view correspondence problem. In the general case, the images can either come from N different cameras which depict (more or less) the same scene or from one and the same camera which is moving relative to the scene. An even more difficult version of the correspondence problem occurs when also the objects in the scene can be in general motion relative to the camera(s).

A typical application of the correspondence problem occurs in image mosaicking when two or more images which only have a smaller overlap are to be stitched into a larger composite image. In this case it is necessary to be able to identify a set of corresponding points in a pair of images in order to calculate the a transformation of one image to stitch it onto the other image.

• Stereoscopy
• Parallax
• Photogrammetry
• Depth perception
• Stereopsis
• Computer vision
• Fundamental matrix

• Middlebury Stereo Vision page

• D. Scharstein and R. Szeliski. A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms. (PDF)