An object model for computer graphics applications should contain two aspects of information: shape and reflectance properties of the object. A number of techniques have been developed for modeling object shapes by observing real objects. In contrast, 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. In this paper, we propose a new method for modeling object reflectance properties, as well as object shapes, by observing real objects. First, an object surface shape is reconstructed by merging multiple range images of the object. By using the reconstructed object shape and a sequence of color images of the object, parameters of a reflection model are estimated in a robust manner. The key point of the proposed method is that, first, the diffuse and specular reflection components are separated from the color image sequence, and then, reflectance parameters of each reflection component are estimated separately. This approach enables estimation of reflectance properties of real objects whose surfaces show specularity as well as diffusely reflected lights. The recovered object shape and reflectance properties are then used for synthesizing object images with realistic shading effects under arbitrary illumination conditions.

For synthesizing realistic images of a real three dimensional object, reflectance properties of the object surface, as well as the object shape, need to be measured. This paper describes one approach to create a three dimensional object model with physically correct reflectance properties by observing a real object. The approach consists of three steps. First, a sequence of range images and color images is measured by rotating a real object on a rotary table with fixed viewing and illumination directions. Then, the object shape is obtained as a collection of triangular patches by merging multiple range images. Secondly, by using the recovered object shape, color pixel intensities of the color image sequence are mapped to the object surface and separated into the diffuse and specular reflection components. Finally, the separated reflection components are used to estimate parameters of the Lambertian reflection model and a simplified Torrance-Sparrow reflection model. We have successfully tested our approach using images of a real object. Synthesized images of the object under arbitrary illumination conditions are shown in this paper.

Introduction The mixed reality technology is considered as one of the key technologies for enhancing a wide variety of applications ranging from manufacturing to entertainment. The mixed reality technology di#ers from the virtual reality technology in that users can feel immersed in a space which consists of not only computer-generated objects but also real objects. Thus seamless integration of the virtual and real worlds is essential for mixed reality in addition to reality of the virtual world. Our e#orts in the mixed reality research span two aspects: how to create models of virtual objects and how to integrate such virtual objects with real scenes. For model creation, wehave developed two methods, the model-based rendering method and the eigen-texture method, both of which automatically create such rendering models by observing real objects. The model-based rendering method #rst analyzes input images of real objects, obtains re#ectance parameters from this analysis, and th

Both photometric and geometric information are important for 3D object recognition. Traditionally, however, few systems utilized both types of information. This is because no single representation is suitable for both types of information. This paper proposes a method for representing both color and geometric information using a common framework, the Spherical Attribute Image (SAI). The SAI maps the values of curvature and color computed at every node of a mesh approximating the object surface onto a spherical image. A model object and an observed surface are computed by finding the rotation that brings their spherical images into correspondence. We show how this matching algorithm can be used for object recognition using both geometric and photometric information. In addition, we describe how the two types of information can be combined in a way that takes into account their actual distribution on the surface. Contents 1 Introduction....................................................

In this paper we present an algorithm for camera and projector calibration of the liquid crystal range finder using space encoding technique. Because the accuracy of calibration is affected by noises, and the variation of position or scale of the reference object in the input images, the least-median-of-squares technique is used in our approach, instead of the conventional least squares method. The computer simulations are performed for various types of the input noise, demonstrating results show that the proposed algorithm is reliable and accurate for non-Gaussian noise. Key Words: Camera Calibration, Least Median Square, Space encoding, Range Finder 1 Introduction In general, it is not easy to extract the 3-D scene information from 2-D images, because the matching of corresponding points is a difficult correlation problem. To alleviate the complexities of passive vision technique and to obtain directly numerical information about the scene, the structured lighting system have been ...

this paper, we consider a new approach for 3D shape and reflectance morphing of two real 3D objects. Our morphing method consists of two components: shape and reflectance property measurement, and smooth interpolation of those measured properties. The measured shape and reflectance parameters are used to compute intermediate shape and reflectance parameters. Finally, the computed shape and reflectance parameters are used to render intermediate images which represent a smooth transition between the two objects.

In this paper we propose a new registration algorithm. The proposed algorithm consists of two steps. The first step is to estimate the transformation parameters among multiple range views, making use of the eigenvectors of the weighted covariance matrix of the 3-D coordinates of data points. The weighting factors are carefully selected to take into account the projection effect caused by different viewpoints. The next step is to register the views iteratively with the estimated transformation parameters as initial values. To solve the correspondence problem, the reverse calibration technique is used, which is adapted to the space encoding rangefinder. The object function, defined by means of the reverse calibration technique, is minimized iteratively. Experimental results show that the proposed algorithm is very fast and efficient. Key words: rangefinder, space encoding technique, reverse calibration, initial estimation, registration 1 Introduction The 3-D shape reconstruction is an ...

This paper describes a controlled method for fusioning several partial models corresponding to the same object, with the purpose of reconstructing a regularized 3D object model. The method takes advantage of previously defined partial modeling structures, in the context of spherical representation.