Uncertainty in 3D Shape Estimation

This paper analyses the uncertainty in the estimation of shape from different cues, specifically motion, stereo, and texture. It is shown that there are computational limitations of a statistical nature that previously have not been recognized. Because there is noise in all the input parameters, we cannot avoid bias. The analysis of shape from multiple views rests on a new constraint which relates image lines and rotation to shape. Because the human visual system has to cope with bias as well, it makes errors. This explains the underestimation of slant found in computational and psychophysical experiments, and demonstrated here for an illusory display. We discuss properties of the best known estimators with regard to the problem, as well as possible avenues for visual systems to deal with the bias. Finally, we show experiments that confirm the theoretical analysis.