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Monocular Segment-Wise Depth: Monocular Depth Estimation Based on a Semantic Segmentation Prior

Atapour-Abarghouei, A.; Breckon, T.P.

Monocular Segment-Wise Depth: Monocular Depth Estimation Based on a Semantic Segmentation Prior Thumbnail


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Abstract

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.

Citation

Atapour-Abarghouei, A., & Breckon, T. (2019). Monocular Segment-Wise Depth: Monocular Depth Estimation Based on a Semantic Segmentation Prior. In 2019 IEEE International Conference on Image Processing (ICIP) ; proceedings (4295-4299). https://doi.org/10.1109/icip.2019.8803551

Conference Name IEEE International Conference on Image Processing
Conference Location Taipei, Taiwen
Start Date Sep 22, 2019
End Date Sep 25, 2019
Acceptance Date Apr 30, 2019
Publication Date Sep 1, 2019
Deposit Date Jun 4, 2019
Publicly Available Date Mar 29, 2024
Pages 4295-4299
Series ISSN 2381-8549
Book Title 2019 IEEE International Conference on Image Processing (ICIP) ; proceedings.
DOI https://doi.org/10.1109/icip.2019.8803551
Additional Information 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. [

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