Stereo matching algorithm based on deep learning: A survey

• Published in: Journal of King Saud University. Computer and information sciences Vol. 34; no. 5; pp. 1663 - 1673
• Main Authors: Hamid, Mohd Saad, Manap, NurulFajar Abd, Hamzah, Rostam Affendi, Kadmin, Ahmad Fauzan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2022; Elsevier
• Subjects: Artificial intelligence; Convolutional neural network; Deep learning; Stereo matching algorithm; Deep learning; Stereo matching algorithm; Artificial intelligence; Convolutional neural network
• ISSN: 1319-1578; 2213-1248
• DOI: 10.1016/j.jksuci.2020.08.011

The development of stereo matching algorithm is still one of the challenging problems, especially in ill-posed regions. Hence, this article presents a survey on the algorithm frameworks related to the stereo matching algorithm. Based on the early survey that had been conducted, two major frameworks available in current stereo matching algorithm development, they are traditional and artificial intelligence (AI) frameworks. Most of the traditional methods are very low accuracy compared to the AI-based approach. This can be observed in the standard benchmarking dataset, such as from the KITTI and the Middlebury, where AI methods rank at the top of the accuracy list. Additionally, the trend for solving computer vision problems uses AI or machine learning tools that become more apparent in recent years. Thus, this paper is focusing on the survey between the deep learning frameworks, which is one of the machine learning tools related to the convolutional neural network (CNN). Several mixed approaches between CNN based method and traditional handcraft method, as well as the end to end CNN method also discussed in this paper.