Perception of shape in two and three dimensions

人类的视觉系统使我们能够从视网膜上的二维（2D）图像中感知物体的三维（3D）形状。 关于物体形状的最重要的视觉信息之一是图像阴影：由于局部表面补丁相对于光源的方向差异，图像中不同位置的强度变化。 从图像阴影中恢复物体形状的问题是困难的，因为它是欠确定的：任何给定的图像都可能是由无限多的照明，3D表面和表面反射模式的排列中的任何一种产生的。 因此，为了从图像阴影中感知3D物体形状，任何自然或人工视觉系统都必须依赖于假设，至少隐含地，关于2D图像的3D解释是合理的还是不合理的。 拟议的研究将调查人类视觉系统对自然场景的假设，以便从2D图像中感知3D形状，以及这些假设与自然场景的最佳假设相比如何。 各组成项目如下。 首先，我们将制定一个自然照明的统计描述，这将回答一些问题，如直接或漫射自然照明往往是。 为此，我们将构建一种新颖的多方向光度计，它可以同时测量多个方向的光强。 其次，我们将制定一个统计模型的自然表面补丁，这将描述的形状和表面反射模式的小表面补丁，通常在自然场景中发现。 该模型将基于大量随机选择的对象的3D数字扫描。 最后，我们将运行心理物理实验来测量人类在3D形状感知任务中的表现，使用与我们测量的照明和表面统计数据相匹配的视觉刺激，或者以各种方式偏离这些统计数据。 从人类在这些任务中的表现模式，我们将推断人类观察者依赖于世界的哪些属性来准确感知3D形状。

The human visual system enables us to perceive the three-dimensional (3D) shapes of objects from the two-dimensional (2D) images on our retinas.  One of the most important kinds of visual information about object shape is image shading:  the variation of intensity from place to place in an image, due to differences in the orientation of local surface patches relative to a light source.  The problem of recovering object shape from image shading is difficult, because it is underdetermined:  any given image could have been produced by any of an infinite number of arrangements of lighting, 3D surfaces, and surface reflectance patterns.  Consequently, to perceive 3D object shape from image shading, any natural or artificial visual system must rely on assumptions, at least implicitly, about what 3D interpretations of 2D images are plausible or implausible.      The proposed research will investigate what assumptions the human visual system makes about natural scenes in order to perceive 3D shape from 2D images, and how these assumptions compare to the optimal assumptions for natural scenes.      The component projects are as follows.  First, we will formulate a statistical description of natural lighting, which will answer questions such as how direct or diffuse natural lighting tends to be.  For this purpose we will construct a novel multidirectional photometer, that measures light intensity in many directions at once.  Second, we will formulate a statistical model of natural surface patches, which will describe the shapes and surface reflectance patterns of small surface patches that are typically found in natural scenes.  This model will be based on 3D digital scans of a large number of randomly selected objects.  Finally, we will run psychophysical experiments to measure human performance in 3D shape perception tasks, using visual stimuli that either match the illumination and surface statistics that we will have measured, or depart from these statistics in various ways.  From the pattern of human performance across these tasks, we will infer what properties of the world human observers rely on to accurately perceive 3D shape.