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Recent automotive vision work has focused almost exclusively on processingforward-facing cameras. However, future autonomous vehicles will not be viablewithout a more comprehensive surround sensing, akin to a human driver, as canbe provided by 360{\deg} panoramic cameras. We present an approach to adaptcontemporary deep network architectures developed on conventional rectilinearimagery to work on equirectangular 360{\deg} panoramic imagery. To address thelack of annotated panoramic automotive datasets availability, we adapt acontemporary automotive dataset, via style and projection transformations, tofacilitate the cross-domain retraining of contemporary algorithms for panoramicimagery. Following this approach we retrain and adapt existing architectures torecover scene depth and 3D pose of vehicles from monocular panoramic imagerywithout any panoramic training labels or calibration parameters. Our approachis evaluated qualitatively on crowd-sourced panoramic images and quantitativelyusing an automotive environment simulator to provide the first benchmark forsuch techniques within panoramic imagery.

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