Self-supervised recurrent depth estimation with attention mechanisms

PeerJ Computer Science. 2022. Vol. 8. Article e865.

Makarov I., Bakhanova M., Nikolenko S., Gerasimova O.

Depth estimation has been an essential task for many computer vision applications, especially in autonomous driving, where safety is paramount. Depth can be estimated not only with traditional supervised learning but also via a self-supervised approach that relies on camera motion and does not require ground truth depth maps. Recently, major improvements have been introduced to make self-supervised depth prediction more precise. However, most existing approaches still focus on single-frame depth estimation, even in the self-supervised setting. Since most methods can operate with frame sequences, we believe that the quality of current models can be significantly improved with the help of information about previous frames. In this work, we study different ways of integrating recurrent blocks and attention mechanisms into a common self-supervised depth estimation pipeline. We propose a set of modifications that utilize temporal information from previous frames and provide new neural network architectures for monocular depth estimation in a self-supervised manner. Our experiments on the KITTI dataset show that proposed modifications can be an effective tool for exploiting temporal information in a depth prediction pipeline.

Keywords: компьютерное зрение, computer vision, Augmented Reality, Autonomous vehicles, Deep Convolutional Neural Networks, depth reconstruction, recurrent neural networks, Self-supervised learning, Attention Mechanism

Publication based on the results of: Methods and computationally efficient algorithms for computer vision and multimodal data analysis (2022)