Computer stereo vision

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Computer stereo vision is the extraction of 3D information from digital images, such as obtained by a CCD camera. By comparing information about a scene from two vantage points, 3D information can be extracted by examination of the relative positions of objects in the two panels. This is similar to the biological process Stereopsis.


In traditional stereo vision, two cameras, displaced horizontally from one another are used to obtain two differing views on a scene, in a manner similar to human binocular vision. By comparing these two images, the relative depth information can be obtained, in the form of disparities, which are inversely proportional to the differences in distance to the objects.

To compare the images, the two views must be superimposed in a stereoscopic device, the image from the right camera being shown to the observer's right eye and from the left one to the left eye.

In real camera systems however, several pre-processing steps are required.[1]

  1. The image must first be removed of distortions, such as barrel distortion to ensure that the observed image is purely projectional.
  2. The image must be projected back to a common plane to allow comparison of the image pairs, known as image rectification.
  3. The displacement of relative features is measured to calculate a disparity map
  4. Optionally, the disparity as observed by the common projection, is converted back to the height map by inversion. Utilising the correct proportionality constant, the height map can be calibrated to provide exact distances.

Active stereo vision[edit]

Active stereo vision is a form of stereo vision which actively employs a light such as a laser or a structured light to simplify the stereo matching problem. The opposed term is passive stereo vision.


3D stereo displays finds many applications in entertainment, information transfer and automated systems. Stereo vision is highly important in fields such as robotics, to extract information about the relative position of 3D objects in the vicinity of autonomous systems. Other applications for robotics include object recognition, where depth information allows for the system to separate occluding image components, such as one chair in front of another, which the robot may otherwise not be able to distinguish as a separate object by any other criteria.

Scientific applications for digital stereo vision include the extraction of information from aerial surveys, for calculation of contour maps or even geometry extraction for 3D building mapping, or calculation of 3D heliographical information such as obtained by the NASA STEREO project.

See also[edit]


  1. ^ Bradski, Gary and Kaehler, Adrian. Learning OpenCV: Computer Vision with the OpenCV Library. O'Reilly. 

External links[edit]