Integration of ontology reasoning-based monocular cues in deep learning modeling for single image depth estimation in urban driving scenarios

Humans are able to estimate the depth of objects in their environment even using only one eye through the use of what are known as monocular cues. In this paper, we aim to integrate human knowledge and human-like reasoning used for monocular depth estimation within deep neural networks. The idea is to support the network in order to help it learn in an explicit and fast way the essential cues for the target task. For this purpose, we investigate the possibility of directly integrating geometric, semantic, and contextual information into the monocular depth estimation process. We propose exploiting an ontology model in a deep learning context to represent the urban environment as a structured set of concepts linked with semantic relationships. Monocular cues information are extracted through reasoning performed on the proposed ontology and are fed together with the RGB image in a multistream way into the deep neural network for depth estimation. Our approach is validated and evaluated on widespread benchmark datasets: KITTI, CityScapes, and AppolloScape. The obtained results show that the proposed method improves upon the state-of-the-art monocular depth estimation deep models and shows promising results regarding cross-evaluation, mainly for unseen driving scenarios.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper aims to integrate human knowledge and human-like reasoning used for monocular depth estimation within deep neural networks. The proposed approach involves directly integrating geometric, semantic, and contextual information into the monocular depth estimation process using an ontology model in a deep learning context. Monocular cues information is extracted through reasoning performed on the proposed ontology and combined with RGB image as input to the deep neural network for depth estimation. The experimental results show that the proposed method improves upon state-of-the-art monocular depth estimation deep models and yields promising results for cross-evaluation, especially for unseen driving scenarios.