Shape from Shading by Model Inclusive Learning - Simultaneous Estimation of Reflection Parameters
Yasuaki Kuroe, Hajimu Kawakami · 2013
The problem of recovering shape from shading is important in computer vision and robotics and many studies have been done. We have already proposed a versatile method of solving the problem by model inclusive learning of neural networks. In the proposed learning method, the image-formation model is included in the learning loop of neural networks. The method is versatile in the sense that it can solve the problem in various circumstances. Almost all of the methods proposed so far assume that surface reflection properties for a target object are known a priori. It is, however, very difficult to obtain those properties exactly. In this paper we propose a method to resolve this problem by extending our previous method. The proposed method is a model inclusive learning of neural networks which makes it possible to recover shape and estimate reflection parameters of an object simultaneously. The performance of the proposed method is demonstrated through experiments.