  Many surface cues support three-dimensional shape perception, but people can
sometimes still see shape when these features are missing -- in extreme cases,
even when an object is completely occluded, as when covered with a draped
cloth. We propose a framework for 3D shape perception that explains perception
in both typical and atypical cases as analysis-by-synthesis, or inference in a
generative model of image formation: the model integrates intuitive physics to
explain how shape can be inferred from deformations it causes to other objects,
as in cloth-draping. Behavioral and computational studies comparing this
account with several alternatives show that it best matches human observers in
both accuracy and response times, and is the only model that correlates
significantly with human performance on difficult discriminations. Our results
suggest that bottom-up deep neural network models are not fully adequate
accounts of human shape perception, and point to how machine vision systems
might achieve more human-like robustness.
