Combining Shape from Shading and Stereo Using Human Vision Model Combining Shape from Shading and Stereo Using Human Vision Model

Stereo algorithms suuer from the lack of local surface texture due to smoothness of depth constraint , or local miss-matches in disparity estimates. Thus, the stereo methods only provide a coarse depth map which can be associated with a low pass image of the depth map. On the other hand, shape from shading algorithms produce better estimates of local surface areas, but some of them have problems with variable albedo and spherical surfaces. Thus, shape from shading methods produce better detailed depth information, and can be associated with the high pass image of the depth map image. In order to compute a better depth map, we present a method for integrating the high frequency information from the shape from shading and the low frequency information from stereo. Our method is motivated by the human vision system, and follows Hall and Hall's model. The proposed algorithm is very simple, takes about :7 seconds for a 128 128 image on a Sun SparcStation-1, is non-iterative, and does not use any thresholds. The results obtained with a variety of synthetic and real images are discussed. The quality of depth obtained by integrating shading and stereo is compared with the ground truth (range image) using average surface gradient error measure, and improvement ranging from 30% to 50% over stereo, and from 65% to 98% over shading is demonstrated.