Accidental Turntables: Learning 3D Pose by Watching Objects Turn

Zezhou Cheng, Matheus Gadelha, Subhransu Maji

Abstract: 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, provide 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 the 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 serve as a benchmark for 3D pose estimation.