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The accuracy of depth judgments that are based on binocular disparity or structure from motion (motion parallax and object rotation) was studied in 3 experiments. In Experiment 1, depth judgments were recorded for computer simulations of cones specified by binocular disparity, motion parallax, or stereokinesis. In Experiment 2, judgments were recorded for real cones in a structured environment, with depth information from binocular disparity, motion parallax, or object rotation about the y-axis. In both of these experiments, judgments from binocular disparity information were quite accurate, but judgments on the basis of geometrically equivalent or more robust motion information reflected poor recovery of quantitative depth information. A 3rd experiment demonstrated stereoscopic depth constancy for distances of 1 to 3 m using real objects in a well-illuminated, structured viewing environment in which monocular depth cues (e.g., shading) were minimized.

In binocular systems, vergence is the process of adjusting the angle between the eyes (or cameras) so that both eyes are directed at the same world point. Its utility is most obvious for foveate systems such as the human visual system, but it is a useful strategy for non-foveate binocular robots as well. This paper discusses the vergence problem and outlines a general approach to vergence control, consisting of a control loop driven by an algorithm that estimates the vergence error. As a case study, this approach is used to verge the eyes of the Rochester Robot in real time. Vergence error is estimated with the cepstral disparity filter, The cepstral filter is analyzed, and it is shown in this application to be equivalent to correlation with an adaptive prefilter; carrying this idea to its logical conclusion converts the cepstral filter into phase correlation. The demonstration system uses a PD controller in cascade with the error estimator. An efficient real-time implementation of the error estimator is discussed, and empirical measurements of the performance of both the disparity estimator and the overal1 system are presented.

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