A computational model of depth perception based on headcentric disparity

It is now well established that depth is coded by local horizontal disparity and global vertical disparity. We present a computational model which explains how depth is extracted from these two types of disparities. The model uses the two (one for each eye) headcentric directions of binocular targets, derived from retinal signals and oculomotor signals. Headcentric disparity is defined as the difference between headcentric directions of corresponding features in the left and right eye's images. Using Helmholtz's coordinate systems we decompose headcentric disparity into azimuthal and elevational disparity. Elevational disparities of real objects are zero if the signals which contribute to headcentric disparity do not contain any errors. Azimuthal headcentric disparity is a 1D quantity from which an exact equation relating distance and disparity can be derived. The equation is valid for all headcentric directions and for all binocular fixation positions. Such an equation does not exist if disparity is expressed in retinal coordinates. Possible types of errors in oculomotor signals (six) produce global elevational disparity fields which are characterised by different gradients in the azimuthal and elevational directions. Computations show that the elevational disparity fields uniquely characterise both the type and size of the errors in oculomotor signals. Our model uses a measure of the global elevational disparity field together with local azimuthal disparity to accurately derive headcentric distance throughout the visual field. The model explains existing data on whole-field disparity transformations as well as hitherto unexplained aspects of stereoscopic depth perception.