An Improved Depth Estimation using Stereo Matching and Disparity Refinement Based on Deep Learning

Abstract

Stereo matching techniques are a vital subject in computer vision. It focuses on finding accurate disparity maps that find its use in several applications namely reconstruction of a 3D scene, navigation of robot, augmented reality. It is a method of obtaining corresponding matching point in stereo images to get disparity map. With additional details, this disparity map could be converted into a depth of a scene. Obtaining an efficient disparity map in the texture less, occluded, and discontinuous areas is a difficult job. A matching cost using an improvised Census transform and an optimization framework is proposed to produce an initial disparity map. The classic Census transform focus on the value of pixel at the center. If this pixel is prone to noisy condition, then the census encoding may differ which leads to mismatches. To overcome this issue an improved census transform based on weighted sum values of the neighborhood pixels is proposed which suppresses the noise during stereo matching. Additionally, a deep learning based disparity refinement technique using the generative adversarial network to handle texture less, occluded, and discontinuous areas is proposed. The suggested method offers cutting-edge performance in terms of both qualitative and quantitative outcomes.