A framework for the reconstruction of smooth surface shapes from shading images is presented. The method is based on using a backpropagation based neural network for learning brightness patterns and associating them with range data. The network is designed to reconstruct surface range from localized intensity patches of 7/spl times/7 pixels. Two methods for training the network are investigated, one based on a novel weight diffusion process which enforces a local smoothness constraint and the other using the eigen coefficients of the input and output patterns which make the training computationally efficient. An elegant and simple method for integrating reconstructed surface patches by minimizing the sum squared error in overlapped areas is derived. Results are shown for reconstruction of simple shapes like cylinders, hyperboloids and paraboloids as well as complex shapes like facial structure from intensity images.
['Jezekiel Ben-Arie', 'Dibyendu Nandy']
A neural network approach for reconstructing surface shape from shading