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Veritatem Dies Aperit - Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach

Lookup NU author(s): Dr Amir Atapour AbarghoueiORCiD



This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by IEEE, 2019.

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Robust geometric and semantic scene understanding is ever more important in many real-world applications such as autonomous driving and robotic navigation. In this paper, we propose a multi-task learning-based approach capable of jointly performing geometric and semantic scene understanding, namely depth prediction (monocular depth estimation and depth completion) and semantic scene segmentation. Within a single temporally constrained recurrent network, our approach uniquely takes advantage of a complex series of skip connections, adversarial training and the temporal constraint of sequential frame recurrence to produce consistent depth and semantic class labels simultaneously. Extensive experimental evaluation demonstrates the efficacy of our approach compared to other contemporary state-of-the-art techniques.

Publication metadata

Author(s): Atapour-Abarghouei A, Breckon TP

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019)

Year of Conference: 2019

Pages: 3373-3384

Online publication date: 09/01/2020

Acceptance date: 11/03/2019

Date deposited: 06/02/2021

ISSN: 2575-7075

Publisher: IEEE


DOI: 10.1109/CVPR.2019.00349

Library holdings: Search Newcastle University Library for this item

ISBN: 9781728132938