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Summary: Abstract
Humans perceive some objects more complex than
others and learning or describing a particular object is
directly related to the judged complexity. Towards the
goal of understanding why the geometry of some 3D
objects appear more complex than others, we conducted a
psychophysical study and identified contributing
attributes. Our experiments conclude that surface
variation, symmetry, part count, simpler part
decomposability, intricate details and topology are six
significant dimensions that influence 3D visual shape
complexity. With that knowledge, we present a method of
quantifying complexity and show that the informational
aspect of Shannon's theory agrees with the human notion
of shape complexity.
1. Introduction
The cognitive study of 3D shape perception has helped
computer vision researchers try and emulate activities like
object recognition [3] and scene analysis [5]. However, in
the interface between cognitive science and computer
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