Skip to main content

Research Repository

Advanced Search

Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery

Payen de La Garanderie, Grégoire; Atapour Abarghouei, Amir; Breckon, Toby P.

Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery Thumbnail


Authors

Grégoire Payen de La Garanderie

Toby P. Breckon



Contributors

Vittorio Ferrari
Editor

Martial Hebert
Editor

Cristian Sminchisescu
Editor

Yair Weiss
Editor

Abstract

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.

Citation

Payen de La Garanderie, G., Atapour Abarghouei, A., & Breckon, T. P. (2018). Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery. In V. Ferrari, M. Hebert, C. Sminchisescu, & Y. Weiss (Eds.), Computer Vision – ECCV 2018 : 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XII (812-830). Springer Verlag. https://doi.org/10.1007/978-3-030-01261-8_48

Online Publication Date Oct 6, 2018
Publication Date Oct 6, 2018
Deposit Date Oct 12, 2018
Publicly Available Date Mar 29, 2024
Publisher Springer Verlag
Pages 812-830
Series Title Lecture notes in computer science
Book Title Computer Vision – ECCV 2018 : 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XII.
ISBN 9783030012601
DOI https://doi.org/10.1007/978-3-030-01261-8_48

Files





You might also like



Downloadable Citations