Publication details for Professor Toby BreckonAtapour-Abarghouei, A. & Breckon, T.P. (2019), To complete or to estimate, that is the question: A Multi-Task Depth Completion and Monocular Depth Estimation, International Conference on 3D Vision. Quebec, IEEE, Piscataway, NJ, 183-193.
- Publication type: Conference Paper
- ISSN/ISBN: 2475-7888, 9781728131313
- DOI: 10.1109/3DV.2019.00029
- Further publication details on publisher web site
- Durham Research Online (DRO) - may include full text
Author(s) from Durham
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.