Abstract
This paper reviews the development of stereo matching and semantic matching in the field of image correspondence. The existed matching methods of these two kinds of matching problems are discussed and summarized. Since 2014, technologies based on data-driven and deep learning have played an important role in these two types of matching problems, which accelerates the development of image correspondence technology. This paper discusses stereo matching from three perspectives: local stereo matching, global stereo matching, and stereo matching based on neural networks. Besides, this paper divides semantic matching methods into two categories: parametric semantic matching and nonparametric semantic matching. By reviewing and tracking the research development of these two matching problems, this paper provides good navigation for people who are new to the image correspondence field.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
