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Inferring Depth from Single Images in Natural Scenes

The inverse optics problem is one of the oldest and most well known problems in visual perception. Inferring the underlying sources of visual images has no analytic solution. Recent work on brightness, color, and form has suggested that visual percepts represent the probable sources of visual stimuli and not the stimuli as such, suggesting an empirical theory of visual perception. Here I explore this idea by framing the perception of depth as a machine learning problem. I apply two algorithms with varying levels of model complexity, compare their ability to infer depth, both with each other and with the best previous solutions.