  We propose a technique for learning single-view 3D object pose estimation
models by utilizing a new source of data -- in-the-wild videos where objects
turn. Such videos are prevalent in practice (e.g., cars in roundabouts,
airplanes near runways) and easy to collect. We show that classical
structure-from-motion algorithms, coupled with the recent advances in instance
detection and feature matching, provides surprisingly accurate relative 3D pose
estimation on such videos. We propose a multi-stage training scheme that first
learns a canonical pose across a collection of videos and then supervises a
model for single-view pose estimation. The proposed technique achieves
competitive performance with respect to existing state-of-the-art on standard
benchmarks for 3D pose estimation, without requiring any pose labels during
training. We also contribute an Accidental Turntables Dataset, containing a
challenging set of 41,212 images of cars in cluttered backgrounds, motion blur
and illumination changes that serves as a benchmark for 3D pose estimation.
