Roughness Estimation and Image Rendering for Glossy Object Surface.

IF 2.7 Q3 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY
Shoji Tominaga, Motonori Doi, Hideaki Sakai
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引用次数: 0

Abstract

We study the relationship between the physical surface roughness of the glossy surfaces of dielectric objects and the roughness parameter in image rendering. The former refers to a measure of the microscopic surface structure of a real object's surface. The latter is a model parameter used to produce the realistic appearance of objects. The target dielectric objects to analyze the surface roughness are handcrafted lacquer plates with controlled surface glossiness, as well as several plastics and lacquer products from everyday life. We first define the physical surface roughness as the standard deviation of the surface normal, and provide the computational procedure. We use a laser scanning system to obtain the precise surface height information at tiny flat areas of a surface. Next, a method is developed for estimating the surface roughness parameter based on images taken of the surface with a camera. With a simple setup for observing a glossy flat surface, we estimate the roughness parameter by fitting the Beckmann function to the image intensity distribution in the observed HDR image using the least squares method. A linear relationship is then found between the measurement-based surface roughness and image-based surface roughness. We present applications to glossy objects with curved surfaces.

光滑物体表面粗糙度估计与图像绘制。
研究了电介质物体光滑表面的物理表面粗糙度与图像绘制中粗糙度参数的关系。前者指的是对真实物体表面微观表面结构的测量。后者是一个模型参数,用于产生对象的逼真外观。分析表面粗糙度的目标介质对象是控制表面光泽度的手工制作的漆板,以及日常生活中的几种塑料和漆制品。首先将物理表面粗糙度定义为表面法向的标准差,并给出了计算过程。我们使用激光扫描系统在表面的微小平坦区域获得精确的表面高度信息。其次,提出了一种基于相机拍摄的表面图像估计表面粗糙度参数的方法。通过简单的观察光滑平面的设置,我们通过使用最小二乘法将Beckmann函数拟合到观察到的HDR图像中的图像强度分布来估计粗糙度参数。然后发现基于测量的表面粗糙度和基于图像的表面粗糙度之间存在线性关系。我们提出了具有曲面的光滑物体的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Imaging
Journal of Imaging Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
5.90
自引率
6.20%
发文量
303
审稿时长
7 weeks
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