Versatile neural network method for recovering shape from shading by model inclusive learning

The problem of recovering shape from shading is important in computer vision and robotics. In this paper, we propose a versatile method of solving the problem by neural networks. We introduce a mathematical model, which we call `image-formation model´, expressing the process that the image is formed from an object surface. We formulate the problem as a model inclusive learning problem of neural networks and propose a method to solve it. In the proposed learning method, the image-formation model is included in the learning loop of neural networks. The proposed method is versatile in the sense that it can solve the problem in various circumstances. The effectiveness of the proposed method is shown through experiments performed in various circumstances.