Accurate real-time neural disparity MAP estimation with FPGA

We propose in this paper a new method for real-time dense disparity map computing using a stereo pair of rectified images. Based on the neural network and Disparity Space Image (DSI) data structure, the disparity map computing consists of two main steps: initial disparity map estimation by combining the neuronal network and the DSI structure, and its refinement. Four improvements are introduced so that an accurate and fast result will be reached. The first one concerns the proposition of a new strategy in order to optimize the computation time of the initial disparity map. In the second one, a specific treatment is proposed in order to obtain more accurate disparity for the neighboring pixels to boundaries. The third one, it concerns the pixel similarity measure for matching score computation and it consists of using in addition to the traditional pixel intensities, the magnitude and orientation of the gradients providing more accuracy. Finally, the processing time of the method has been decreased consequently to our implementation of some critical steps on FPGAs. Experimental results on real datasets are conducted and a comparative evaluation of the obtained results relative to the state-of-art methods is presented.

► Initial disparity map is estimated by combining neuronal network and DSI structure.
► Final disparity map is obtained by refinement of initial disparity map.
► A specific treatment is applied for the neighboring pixels to boundaries.
► Intensities, magnitude and orientation of gradients are used for similarity measure.
► Processing time has been decreased by implementing some critical steps on FPGAs.