Integrating stereo and shape from shading
This paper presents a new method for integrating different low level vision modules, stereo and shape from shading, in order to improve the 3D reconstruction of visible surfaces of objects from intensity images. The integration process is based on correcting the 3D visible surface obtained from shape from shading using the sparse depth measurements from the stereo module by fitting a surface into the difference between the two data sets. A feedforward neural network is used to fit a surface to the error difference. An extended Kalman filter algorithm is used for the network learning. It is found that the integration of sparse depth measurements has greatly enhanced the 3D visible surface obtained from shape from shading in terms of metric measurements.