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.

提出了一个 3D 形状感知的框架，它在分析综合或图像形成的生成模型中解释了典型和非典型情况下的感知，分析和计算研究表明这种方法最符合人类观察者的准确性和响应时间，在困难判别方面与人类表现相关性显著，并提示深度神经网络等机器视觉系统如何实现更具有人类特色的稳健性。