Depth prediction by deep learning

Sammanfattning: Knowing the depth information is of critical importance in scene understanding for several industrial projects such as self-driving cars for instance. Where depth inference from a single still image has taken a prominent place in recent studies with the outcome of deep learning methods, practical cases often offer useful additional information that should be considered early in the architecture of the design to benefit from them in order to improve quality and robustness of the estimates. Hence, this thesis proposes a deep fully convolutional network which allows to exploit the informations of either stereo or monocular temporal sequences, along with a novel training procedure which takes multi-scale optimization into account. Indeed, this thesis found that using multi-scale information all along the network is of prime importance for accurate depth estimation and greatly improves performances, allowing to obtain new state-of-theart results on both synthetic data using Virtual KITTI and also on realimages with the challenging KITTI dataset.