On the synergies between machine learning and stereo
Matteo Poggi
(University of Bologna, Italy)
Abstract:
Stereo matching is one of the longest-standing problems in computer
vision and boasts over 30 years of studies and research. Although often
in competition with active depth sensors and other image-based
techniques, the advent of machine learning and the rapid spread of deep
learning since 2014 rejuvenated this field with new exciting trends and
applications unthinkable until a few years ago. Interestingly, the
relationship between these two worlds is two-way. While machine/deep
learning notably pushed forward the state-of-the-art of stereo matching,
stereo enabled new ground-breaking methodologies such as self-supervised
monocular depth. In this talk, we will give an overview of recent works
leveraging on the synergies between the two, opening new research
directions in the context of self-