Noise causes slant underestimation in stereo and motion

This paper discusses a problem, which is inherent in the estimation of 3D shape (surface normals) from multiple views. Noise in the image signal causes bias, which may result in substantial errors in the parameter estimation. The bias predicts the underestimation of slant found in psychophysical and computational experiments. Specifically, we analyze the estimation of 3D shape from motion and stereo using orientation disparity. For the case of stereo, we show that bias predicts the anisotropy in the perception of horizontal and vertical slant. For the case of 3D motion we demonstrate the bias by means of a new illusory display. Finally, we discuss statistically optimal strategies for the problem and suggest possible avenues for visual systems to deal with the bias.

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