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Durham University

Computer Science


Publication details for Professor Toby Breckon

Atapour-Abarghouei, A. & Breckon, T.P. (2019), Monocular Segment-Wise Depth: Monocular Depth Estimation Based on a Semantic Segmentation Prior, IEEE International Conference on Image Processing. Taipei, Taiwan, IEEE, Piscataway, NJ, 4295-4299.

Author(s) from Durham


Monocular depth estimation using novel learning-based approaches
has recently emerged as a promising potential alternative
to more conventional 3D scene capture technologies
within real-world scenarios. Many such solutions often depend
on large quantities of ground truth depth data, which is
rare and often intractable to obtain. Others attempt to estimate
disparity as an intermediary step using a secondary supervisory
signal, leading to blurring and other undesirable artefacts.
In this paper, we propose a monocular depth estimation
approach, which employs a jointly-trained pixel-wise semantic
understanding step to estimate depth for individuallyselected
groups of objects (segments) within the scene. The
separate depth outputs are efficiently fused to generate the final
result. This creates more simplistic learning objectives for
the jointly-trained individual networks, leading to more accurate
overall depth. Extensive experimentation demonstrates
the efficacy of the proposed approach compared to contemporary
state-of-the-art techniques within the literature.


Conference dates September 22-25 2019.

Not sure about pub date. IEEE xplore states: Date Added to IEEE Xplore: 26 August 2019 - but that's before the conference. [