Stereo matching algorithm using deep learning and edge-preserving filter for machine vision

Abstract

Machine vision research began with a single-camera system, but these systems had various limitations from having just one point-of-view of the environment and no depth information, therefore stereo cameras were invented. This paper proposes a hybrid method of a stereo matching algorithm with the goal of generating an accurate disparity map critical for applications such as 3D surface reconstruction and robot navigation to name a few. Convolutional neural network (CNN) is utilised to generate the matching cost, which is then input into cost aggregation to increase accuracy with the help of a bilateral filter (BF). Winner-take-all (WTA) is used to generate the preliminary disparity map. An edge-preserving filter (EPF) is applied to that output based on a transform that defines an isometry between curves on the 2D image manifold in 5D and the real line to eliminate these artefacts. The transform warps the input signal adaptively to allow linear 1D filtering. Due to the filter's resistance to high contrast and brightness, it is effective in refining and removing noise from the output image. Based on experimental research employing a Middlebury standard validation benchmark, this approach gives high accuracy with an average non-occluded error of 6.71% comparable to other published methods.