In this paper, a method for reconstructing a 3-D shape of an object from a 2-D shading image using a Genetic Algorithm (GA), which is an optimizing technique modeled on the process of living thing's evolution, is proposed. In the proposed method, the problem of reconstructing shapes from shading images can be regarded as a kind of optimization problem, that is to search for the shape providing the most similar shading image to the shading image of the original object, and then, the GA is applied to solving the optimization problem. Each candidate of the shape corresponds to an individual in population which is formed by a lot of individuals. The fitness value of each individual is decided from the similarity of the shading image provided by the candidate of the shape to the measured shading image. The individuals with higher fitness values are selected according to mechanisms of natural selection of the GA, and then, an individual having the highest fitness value can be found finally. The 3-D shape corresponding to the individual is regarded as the reconstructed shape of the object. Using the proposed method, 3-D shapes are reconstructed from shading images of synthesized objects which are calculated by computer simulation. The results of the reconstruction demonstrate the effectiveness of the proposed method.

This article introduces efficient inference technology as an important element in applying deep learning to business and an inference cloud service that is combined with NTT Group assets such as telephone exchange buildings and base stations.

In this paper, a method for reconstructing a 3-D shape of an object from a 2-D shading image using a Genetic Algorithm (GA), which is an optimizing technique modeled on the process of living thing's evolution, is proposed. In the proposed method, the problem of reconstructing shapes from shading images can be regarded as a kind of optimization problem, that is to search for the shape providing the most similar shading image to the shading image of the original object, and then, the GA is applied to solving the optimization problem. Each candidate of the shape corresponds to an individual in population which is formed by a lot of individuals. The fitness value of each individual is decided from the similarity of the shading image provided by the candidate of the shape to the measured shading image. The individuals with higher fitness values are selected according to mechanisms of natural selection of the GA, and then, an individual having the highest fitness value can be found finally. The 3-D shape corresponding to the individual is regarded as the reconstructed shape of the object. Using the proposed method, 3-D shapes are reconstructed from shading images of synthesized objects which are calculated by computer simulation. The results of the reconstruction demonstrate the effectiveness of the proposed method.