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