Surface Color Estimation of Large Scale Diffuse Objects under Outdoor Environment

Digital three-dimensional models created by computer vision and graphics techniques are becoming widely used for a variety of purposes. Specifically, modeling cultural heritage objects has attracted considerable attention, since such objects are worth preserving, and the data can be utilized for restoration when an object faces the crisis of collapse. Automation for creating 3D models has therefore attracted much interest, since most models are currently created by manual operation, adding significantly to the cost. Creating an accurate model of an object requires knowledge of the object's shape and surface reflectance. Acquiring shape information is facilitated by the development of sensors and the progress of data processing algorithms, but acquiring surface reflectance properties remains a challenge, specifically with outdoor objects. This paper targets large-scale objects such as architectural structures in an outdoor environment. The size of target objects may be as much as 100 m by 100 m by 50 m. Measuring the surface properties of such huge objects is a challenge. The appearance of an object can be modeled by mapping image textures to the known shape of the object. However, to achieve consistent colors among image textures, the effect of illumination has to be removed before mapping these textures by using surface color estimation and surface reflectance estimation techniques. Two methods that calculate a surface color by a pixel-based operation are presented. Most previous methods assume uniform illumination in a scene, but this is not always true in images with shadows or with curved objects. The proposed methods enable pixel-based operation by utilizing illumination change. Two models of illumination colors that we introduce enable a surface color to be uniquely determined from two pixel values. First, the paper proposes a method that uses blackbody radiation and analyzes the stability and practicality of the method. Then, a more practical method is proposed that can perform robust estimation using a statistical model derived from outdoor illumination data. Robust estimation is achieved by introducing the plausible range of outdoor illumination colors. In practical situation, surface reflectance would be required for relighting purposes. A method is presented to estimate surface reflectance from spherical images with known shape information. Spherical images have nearly a 360-degree field of view; they capture target objects and surrounding illumination at one shot. Therefore they do not require specific apparatus or calibration of exposure times, apertures, and camera gain factors. Furthermore, geometric calibration between an image and shape information becomes robust owing to the characteristic of a spherical camera. Measurement and data-processing cost will be decreased by the method compared to previous methods that need elaborate procedures. This is critical specifically for large-scale objects. The main contribution of this thesis is that the author has proposed three methods that estimate surface properties of an object. It can be summarized by the three following points: First, the research provides insights into the stability and practicality of pixel-based surface color estimation. Second, a pixel-based method for surface color estimation has been developed that is robust and accurate even for real outdoor objects. None of the conventional methods can perform a pixel-based operation with higher accuracy than the proposed method. Third, an efficient method has been developed that estimates surface reflectance of large-scale objects under outdoor environment. The proposed techniques form the foundation for developing a system that models the appearance of a large-scale object in an outdoor environment.報告番号: 甲23943 ; 学位授与年月日: 2008-03-24 ; 学位の種別: 課程博士 ; 学位の種類: 博士(情報理工学) ; 学位記番号: 博情第188号 ; 研究科・専攻: 情報理工学系研究科電子情報学専