Improvement of stereo matching algorithm for 3D surface reconstruction

• Published in: Signal processing. Image communication Vol. 65; pp. 165 - 172
• Main Authors: Hamzah, Rostam Affendi, Kadmin, A. Fauzan, Hamid, M. Saad, Ghani, S. Fakhar A., Ibrahim, Haidi
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.07.2018; Elsevier BV
• Subjects: 3D reconstruction; Adaptive filters; Adaptive support weight; Algorithms; Edge joints; Gradient matching; Guided filter; Image processing systems; Noise reduction; Reconstruction; Stereo matching; Stereoscopy; Surface matching; Three dimensional imaging; Vision systems; Gradient matching; Guided filter; 3D reconstruction; Stereo matching; Adaptive support weight
• ISSN: 0923-5965; 1879-2677
• DOI: 10.1016/j.image.2018.04.001

The stereo matching algorithm is one of the important methods for 3D surface reconstruction. A stereo matching process produces a disparity map which provides the depth of information required in 3D reconstruction. This map consists of disparity values of two corresponding points. Furthermore, the accuracy of 3D reconstruction depends on how precise the disparity being estimated on each pixel location. To get a good 3D reconstruction result, the propose stereo matching algorithm must be strong against the radiometric differences and edge distortions. Hence, this article proposes a new stereo matching algorithm with high accuracy for 3D surface reconstruction. First stage, Sum of Gradient Matching (SG) is proposed which uses magnitude differences with fixed window size. The gradient matching is strong against the radiometric distortions due to different characteristics of the input stereo cameras. Second stage, the Adaptive Support Weight (ASW) with iterative Guided Filter (ASW iGF) is proposed to improve the edges of object matching. The last stage, Joint Weighted Guided Filter (JWGF) is suggested to reduce the remaining noise on the disparity map. Based on the standard quantitative benchmarking stereo dataset, the proposed work in this article produces good results and performs much better compared with before the proposed framework. This new algorithm is also competitive with some established methods in the literature. •This article introduces an improved stereo matching algorithm for 3D surface reconstruction.•The algorithm framework is developed using the standard local-based method.•There are three contributions. First, the matching cost computation utilized the gradient based matching technique using a fixed window.•Then, the ASW based on iterative Guided Filter is introduced at cost aggregation to filter the preliminary data.•A new filtering approach at the last stage is presented which is to refine the final disparity map.