In this paper, we introduce silhouette tomography, a novel formulation of X-ray computed tomography that relies only on the geometry of the imaging system. We formulate silhouette tomography mathematically and provide a simple method for obtaining a particular solution to the problem, assuming that any solution exists. We then propose a supervised reconstruction approach that uses a deep neural network to solve the silhouette tomography problem. We present experimental results on a synthetic dataset that demonstrate the effectiveness of the proposed method.
Bidirectional Texture Function (BTF) is one of the methods to reproduce realistic images in Computer Graphics (CG). This is a technique that can be applied to texture mapping with changing lighting and viewing directions and can reproduce realistic appearance by a simple and high-speed processing. However, in the BTF method, a large amount of texture data is generally measured and stored in advance. In this paper, in order to address the problems related to the measurement time and the texture data size in the BTF reproduction, we a method to generate a BTF image dataset using deep learning. We recovery texture images under various azimuth lighting conditions from a single texture image. For achieving this goal, we applied the U-Net to our BTF recovery. The restored and original texture images are compared using SSIM. It will be confirmed that the reproducibility of fabric and wood textures is high.