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Eliminating the dreaded blind spot : adapting 3D object detection and monocular depth estimation to 360° panoramic imagery.

Payen de La Garanderie, G. and Atapour-Abarghouei, A. and Breckon, T.P. (2018) 'Eliminating the dreaded blind spot : adapting 3D object detection and monocular depth estimation to 360° panoramic imagery.', 15th European Conference on Computer Vision (ECCV 2018) Munich, Germany, 8-14 September 2018.


Recent automotive vision work has focused almost exclusively on processing forward-facing cameras. However, future autonomous vehicles will not be viable without a more comprehensive surround sensing, akin to a human driver, as can be provided by 360◦ panoramic cameras. We present an approach to adapt contemporary deep network architectures developed on conventional rectilinear imagery to work on equirectangular 360◦ panoramic imagery. To address the lack of annotated panoramic automotive datasets availability, we adapt contemporary automotive dataset, via style and projection transformations, to facilitate the cross-domain retraining of contemporary algorithms for panoramic imagery. Following this approach we retrain and adapt existing architectures to recover scene depth and 3D pose of vehicles from monocular panoramic imagery without any panoramic training labels or calibration parameters. Our approach is evaluated qualitatively on crowd-sourced panoramic images and quantitatively using an automotive environment simulator to provide the first benchmark for such techniques within panoramic imagery.

Item Type:Conference item (Paper)
Full text:Publisher-imposed embargo
(AM) Accepted Manuscript
File format - PDF
Publisher Web site:
Date accepted:05 July 2018
Date deposited:20 September 2018
Date of first online publication:September 2018
Date first made open access:No date available

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