Xiao Bei, Zhao Shuang, Gkioulekas Ioannis, Bi Wenyan, Bala Kavita
Department of Computer Science, American University, Washington, DC, USA.
Department of Computer Science, University of California, Irvine, Irvine, CA, USA.
J Vis. 2020 Jul 1;20(7):10. doi: 10.1167/jov.20.7.10.
When judging the optical properties of a translucent object, humans often look at sharp geometric features such as edges and thin parts. An analysis of the physics of light transport shows that these sharp geometries are necessary for scientific imaging systems to be able to accurately measure the underlying material optical properties. In this article, we examine whether human perception of translucency is likewise affected by the presence of sharp geometry, by confounding our perceptual inferences about an object's optical properties. We use physically accurate simulations to create visual stimuli of translucent materials with varying shapes and optical properties under different illuminations. We then use these stimuli in psychophysical experiments, where human observers are asked to match an image of a target object by adjusting the material parameters of a match object with different geometric sharpness, lighting, and three-dimensional geometry. We find that the level of geometric sharpness significantly affects perceived translucency by observers. These findings generalize across a few illumination conditions and object shapes. Our results suggest that the perceived translucency of an object depends on both the underlying material's optical parameters and the three-dimensional shape of the object. We also find that models based on image contrast cannot fully predict the perceptual results.
在判断半透明物体的光学特性时,人类通常会观察锐利的几何特征,如边缘和薄的部分。对光传输物理原理的分析表明,这些锐利的几何形状对于科学成像系统准确测量底层材料的光学特性是必要的。在本文中,我们通过混淆我们对物体光学特性的感知推断,研究人类对半透明的感知是否同样受到锐利几何形状的影响。我们使用物理精确的模拟来创建半透明材料在不同光照下具有不同形状和光学特性的视觉刺激。然后,我们在心理物理学实验中使用这些刺激,要求人类观察者通过调整具有不同几何锐利度、光照和三维几何形状的匹配物体的材料参数来匹配目标物体的图像。我们发现几何锐利度水平会显著影响观察者感知到的半透明度。这些发现适用于多种光照条件和物体形状。我们的结果表明,物体的感知半透明度既取决于底层材料的光学参数,也取决于物体的三维形状。我们还发现基于图像对比度的模型不能完全预测感知结果。