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

3D Shape Perception Integrates Intuitive Physics and Analysis-by-Synthesis