Review of Stereo Matching Algorithms Based on Deep Learning
Kun Zhou, Xiangxi Meng, Bo Cheng
Abstract
Stereo vision is a flourishing field, attracting the attention of many researchers. Recently, leveraging on the development of deep learning, stereo matching algorithms have achieved remarkable performance far exceeding traditional approaches. This review presents an overview of different stereo matching algorithms based on deep learning. For convenience, we classified the algorithms into three categories: (1) non-end-to-end learning algorithms, (2) end-to-end learning algorithms, and (3) unsupervised learning algorithms. We have provided a comprehensive coverage of the remarkable approaches in each category and summarized the strengths, weaknesses, and major challenges, respectively. The speed, accuracy, and time consumption were adopted to compare the different algorithms.