Accurate depth estimation using structured light and passive stereo disparity estimation
In this paper we propose a new approach to depth estimation which combines structured light and passive stereo disparity estimation techniques to generate accurate disparity maps in both textured and textureless areas of a scene. We project a structured light pattern with adaptive colors onto the scene and simultaneously capture stereo images with a pair of cameras. By matching points in the projected pattern and the left image we first acquire a sparse disparity map. This sparse disparity map is interpolated and used to initialize a passive stereo disparity algorithm to improve disparity accuracy in textured areas. Finally, this map is interpolated in the textureless areas. By comparing our final map with efficient belief propagation and the initial interpolated disparity map, we show our approach performs better than using passive-only or active-only techniques.