doctoral dissertation, tech. report CMU-RI-TR-97-06, Robotics Institute, Carnegie Mellon University, January, 1997
|This thesis describes the automatic reconstruction of 3D object models from observa-tion of real objects. As a result of the significant advancement of graphics hardware and image rendering algorithms, 3D computer graphics capability has become available even on low-end computers. However, it is often the case that 3D object models are created manu-ally by users. That input process is normally time-consuming and can be a bottleneck for realistic image synthesis. Therefore, techniques to obtain object models automatically by observing real objects could have great significance in practical applications.
For generating realistic images of a 3D object, two aspects of information are neces-sary: the object?s shape and its reflectance properties such as color and specularity. A num-ber of techniques have been developed for modeling object shapes by observing real objects. However, attempts to model reflectance properties of real objects have been rather limited. In most cases, modeled reflectance properties are too simple or too complicated to be used for synthesizing realistic images of the object.
One of the main reasons why modeling of reflectance properties has been unsuccessful, compared with modeling of object shapes, is that both diffusely reflected lights and specu-larly reflected lights, i.e., the diffuse and specular reflection components, are treated together, and therefore, estimation of reflectance properties becomes unreliable. To elimi-nate this problem, the two reflection components should be separated prior to estimation of reflectance properties. For this purpose, we developed a new method called goniochromatic space analysis (GSA) which separates two fundamental reflection components from a color image sequence.
Based on GSA, we studied two approaches for generating 3D models from observation of real objects. For objects with smooth surfaces, we developed a new method which exam-ines a sequence of color images taken under a moving light source. The diffuse and specular reflection components are first separated from the color image sequence; then, object sur-face shapes and reflectance parameters are simultaneously estimated based on the separation results. For creating object models with more complex shapes and reflectance properties, we proposed another method which uses a sequence of range and color images. In this method, GSA is further extended to handle a color image sequence taken by changing object posture.
To extend GSA to a wider range of applications, we also developed a method for shape and reflectance recovery from a sequence of color images taken under solar illumination. The method was designed to handle various problems particular to images taken using solar illuminations, e.g., more complex illumination and shape ambiguity caused by the sun?s coplanar motion.
This thesis presents new approaches for modeling object surface reflectance properties, as well as shapes, by observing real objects in both indoor and outdoor environments. The methods are based on a novel method called goniochromatic space analysis for separating the two fundamental reflection components from a color image sequence.
Associated Center(s) / Consortia:
Vision and Autonomous Systems Center
|Yoichi Sato, "Object Shape and Reflectance Modeling from Color Image Sequence," doctoral dissertation, tech. report CMU-RI-TR-97-06, Robotics Institute, Carnegie Mellon University, January, 1997|
author = "Yoichi Sato",
title = "Object Shape and Reflectance Modeling from Color Image Sequence",
booktitle = "",
school = "Robotics Institute, Carnegie Mellon University",
month = "January",
year = "1997",
address= "Pittsburgh, PA",
|The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.|
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