The reconstruction of static visual forms from sparse dotted samples

This study explored the ability of observers to recognize very sparsely sampled, stereoscopic, dotted surfaces. The observers’ performance exceeded that of a simple surface-fitting algorithm that served as a first approximation model to the solution of the perceptual problem. We explored various attributes ofthe stimulus surfaces, but were unable to find any single attribute that could account for the measured performance. Therefore, we propose that the observers used several global surface attributes jointly to distinguish one form from another. Collectively, these attributes define what is meant by form in this study. We suggest that our results argue for a globally precedent, multidimensional model of form reconstruction as opposed to a local-feature, unidimensional interpretation.