Cookies

We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.


Durham Research Online
You are in:

To complete or to estimate, that is the question: a multi-task depth completion and monocular depth estimation

Atapour-Abarghouei, Amir and Breckon, Toby P. (2019) 'To complete or to estimate, that is the question: a multi-task depth completion and monocular depth estimation.', in Proceedings of 2019 International Conference on 3D Vision (3DV). Piscataway, NJ: IEEE, pp. 183-193.

Abstract

Robust three-dimensional scene understanding is now an ever-growing area of research highly relevant in many real-world applications such as autonomous driving and robotic navigation. In this paper, we propose a multi-task learning-based model capable of performing two tasks:- sparse depth completion (i.e. generating complete dense scene depth given a sparse depth image as the input) and monocular depth estimation (i.e. predicting scene depth from a single RGB image) via two sub-networks jointly trained end to end using data randomly sampled from a publicly available corpus of synthetic and real-world images. The first sub-network generates a sparse depth image by learning lower level features from the scene and the second predicts a full dense depth image of the entire scene, leading to a better geometric and contextual understanding of the scene and, as a result, superior performance of the approach. The entire model can be used to infer complete scene depth from a single RGB image or the second network can be used alone to perform depth completion given a sparse depth input. Using adversarial training, a robust objective function, a deep architecture relying on skip connections and a blend of synthetic and real-world training data, our approach is capable of producing superior high quality scene depth. Extensive experimental evaluation demonstrates the efficacy of our approach compared to contemporary state-of-the-art techniques across both problem domains.

Item Type:Book chapter
Full text:(AM) Accepted Manuscript
Download PDF
(8618Kb)
Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1109/3DV.2019.00029
Publisher statement:© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:30 July 2019
Date deposited:14 August 2019
Date of first online publication:September 2019
Date first made open access:12 November 2019

Save or Share this output

Export:
Export
Look up in GoogleScholar