We propose a method to estimate the surface normal of concave objects. The target object of our method has a specular surface without diffuse reflection. We solve the problem by analyzing the polarization state of the reflected light. The polarization analysis gives a constraint to the surface normal. However, polarization data from a single view has an ambiguity and cannot uniquely determine the surface normal. To solve this problem, the target object should be observed from two or more views. However, the polarization of the light should be analyzed at the same surface point through the different views. This means that both the camera parameters and the surface shape should be known. The camera parameters can be estimated a priori using known corresponding points. However, it is a contradiction that the shape should be known in order to estimate the shape. To resolve this problem, we assume that the target object is almost planar. Under this assumption, the surface normal of the object is uniquely determined. We show that the surface normal of the nonplanar part can also be estimated using the proposed method.
Polarization is a phenomenon that cannot be observed by the human eye, but it provides rich information regarding scenes. The proposed method estimates the surface normal of black specular objects through polarization analysis of reflected light. A unique surface normal cannot be determined from a polarization image observed from a single viewpoint; thus, we observe the object from multiple viewpoints. To analyze the polarization state of the reflected light at the corresponding points when observed from multiple viewpoints, the abstract shape is predetermined using a space carving technique. Unlike a conventional photometric stereo or multiview stereo, which cannot estimate the shape of a black specular object, the proposed method estimates the surface normal and three-dimensional coordinates of black specular objects via polarization analysis and space carving.
In the first part of this paper, we present a method to estimate the shape of transparent objects by using polarization. Few existing methods for this procedure consider internal interreflection, which is a multiple reflection occurring inside the transparent object. Our proposed method considers such interreflection by using the raytracing method. Also, we calculate the polarization state of the light using Mueller calculus. We then combine these methods to produce rendered polarization data. The shape of the object is computed by an iterative framework that minimizes the difference between the obtained polarization data and the rendered polarization data. In the second part of this paper, we present an apparatus to measure the polarization state of the light. To analyze the light, we use a material called PLZT whose material state changes with the applied voltage. We obtain the polarization state of the light by controlling the voltage of the PLZT from the computer. In the last part of this paper, we present some experimental results using the proposed method and apparatus.
Recently, 3D models are used in many fields such as education, medical services, entertainment, art, digital archive, etc., because of the progress of computational time and demand for creating photorealistic virtual model is increasing for higher reality. In computer vision field, a number of techniques have been developed for creating the virtual model by observing the real object in computer vision field. In this paper, we propose the method for creating
photorealistic virtual model by using laser range sensor and polarization based image capture system. We capture the range and color images of the object which is rotated on the rotary table. By using the reconstructed object shape and sequence of color images of the object, parameter of a reflection model are estimated in a robust manner. As a result, then, we can make photorealistic 3D model in consideration of surface reflection. 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. In separation of reflection components, we use polarization filter. 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.