Deep-learning for 3D reconstruction
2021
Depth perception is paramount for many computer vision applications such as autonomous
driving and augmented reality. Despite active sensors (e.g., LiDAR, Time-of-Flight, struc-
tured light) are quite diffused, they have severe shortcomings that could be potentially
addressed by image-based sensors. Concerning this latter category, deep learning has
enabled ground-breaking results in tackling well-known issues affecting the accuracy of
systems inferring depth from a single or multiple images in specific circumstances (e.g.,
low textured regions, depth discontinuities, etc.), but also introduced additional concerns
about the domain shift occurring between training and target environments and the need
of proper ground truth depth labels to be used as the training signals in network learning.
Moreover, despite the copious literature concerning confidence estimation for depth from a
stereo setup, inferring depth uncertainty when dealing with deep networks is still a major
challenge and almost unexplored research area, especially when dealing with a monocular
setup. Finally, computational complexity is another crucial aspect to be considered when
targeting most practical applications and hence is desirable not only to infer reliable depth
data but do so in real-time and with low power requirements even on standard embedded
devices or smartphones.
Therefore, focusing on stereo and monocular setups, this thesis tackles major issues
affecting methodologies to infer depth from images and aims at developing accurate and
efficient frameworks for accurate 3D reconstruction on challenging environments.
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