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{\deg} panoramic cameras. We present an approach to adapt
contemporary deep network architectures developed on conventional rectilinear
imagery to work on equirectangular 360{\deg} panoramic imagery. To address the
lack of annotated panoramic automotive datasets availability, we adapt a
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.

本研究提出了一种处理全景图像的深度学习算法，通过样式和投影转换，重新训练和调整现有的算法，从单目全景图像中恢复场景深度和车辆的三维姿态，大大提高了无人驾驶汽车使用全景摄像头时的准确性。