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

Lookup NU author(s): Dr Amir Atapour AbarghoueiORCiD



This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by Springer, 2018.

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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.

Publication metadata

Author(s): Payen de La Garanderie G, Atapour-Abarghouei A, Breckon TP

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 15th European Conference on Computer Vision (ECCV 2018)

Year of Conference: 2018

Pages: 812-830

Online publication date: 06/10/2018

Acceptance date: 22/07/2018

Date deposited: 06/02/2021

ISSN: 0302-9743

Publisher: Springer


DOI: 10.1007/978-3-030-01261-8_48

Library holdings: Search Newcastle University Library for this item

Series Title: Lecture Notes in Computer Science

ISBN: 9783030012618