Real-time 3D stereo reconstruction for situational awareness

3D reconstruction using stereopairs is still an open research topic in different communities, such as computer vision, research and remote sensing. The handcrafted algorithms have been flanked by a growing number of deep learning based methods in the last decade, giving an additional boost to the performance of these solutions. Despite this big effort, all these solutions do not deliver 100% reliable results and, more than this, take several seconds or even minutes to deliver dense 3D reconstruction from a single stereo pair. Very few algorithms aim to produce reliable dense reconstruction in real-time, despite their importance for autonomous robotic systems.

This MSc project aims to explore new solutions to deliver accurate and reliable stereo reconstructions in real-time. To reach this goal, the existing deep learning solutions available in the literature will be used as a starting point for this work. The performance of the developed algorithm will be assessed using both terrestrial and airborne data to validate its use for terrestrial and aerial robots.