Perception of 3D shape and material properties from patterns of image shading

人类感知研究中最困难的问题之一涉及观察者正确解释图像阴影模式的能力。从可见表面反射到观察点的光受到几个因素的影响。这些因素包括表面形状、照明模式和表面材料的性质。知觉理论的问题是解释如何能够将这些独立的影响分开，以便对每一个影响做出可靠的判断。该项目旨在研究人类观察者如何实现这一点。这项研究有许多潜在的好处。对形状和材料属性的准确感知对于许多基本的人类功能都很重要，例如抓取物体或判断可见表面是否适合提供安全的运动。更好地了解这些过程可能有助于促进训练，以避免因跌倒而受伤。它还可以改善医疗设备中的3D可视化，或帮助视力受损的人检测运动路径中的光滑点或不规则地形特征。该提议的第一个具体目标是测量3D形状的感知如何受到表面材料（例如，玻璃、蜡或塑料）或照明图案。这将通过在物体上的许多探测点处测量局部3D取向来实现，由此可以计算与观察者的判断最一致的最佳拟合表面。第二个具体目标是测量表面材料的感知分类如何受到3D形状和照明模式变化的影响。这将使用一种新的分类任务来实现，在该任务中，观察者被显示为阴影对象，并且需要调整一组滑块来表示他们对所描绘的材料是几种可能的材料类别之一的信心。这些实验的刺激将使用最先进的计算机图形来产生真实的材料的物理精确模拟。这项研究的结果将极大地提高我们目前对特定计算机制的理解，通过该机制，图像阴影模式为感知物体的3D形状和识别其材料属性提供信息。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

One of the most difficult problems in the study of human perception involves the ability of observers to correctly interpret patterns of image shading. The light that reflects from a visible surface toward the point of observation is influenced by several factors. These include the surface shape, the pattern of illumination, and the nature of the surface material. The problem for perceptual theory is to explain how it is possible to tease apart these separate influences in order to make reliable judgments about each one. This project is designed to investigate how this is achieved by human observers. There are many potential benefits of this research. The accurate perception of shape and material properties are important for many basic human functions, such as grasping objects or judging whether visible surfaces are appropriate to afford safe locomotion. A better understanding of these processes may help to facilitate training to avoid injury due to falling. It could also lead to improved 3D visualization in medical devices, or aids for visually impaired individuals that can detect slippery spots or irregular terrain features in the path of locomotion. The first specific aim of this proposal is to measure how the perception of 3D shape is influenced by changes in surface material (e.g., glass, wax or plastic) or the pattern of illumination. This will be achieved by measuring local 3D orientation at numerous probe points on an object, from which it possible to compute a best fitting surface that is most consistent with an observer's judgments. The second specific aim is to measure how the perceptual categorization of surface materials is influenced by changes in 3D shape and the pattern of illumination. This will be achieved using a novel categorization task in which observers are shown a shaded object, and are required to adjust a set of sliders to indicate their confidence that the depicted material is one of several possible material categories. The stimuli for these experiments will be created using state-of-the-art computer graphics to produce physically accurate simulations of real materials. The results of this research will greatly improve our current understanding of the specific computational mechanisms by which patterns of image shading provide information for perceiving the 3D shapes of objects and the identification of their material properties.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.