Accidental Turntables: Learning 3D Pose by Watching Objects Turn

• URL: http://arxiv.org/abs/2212.06300v1
• Date: Tue, 13 Dec 2022 00:46:26 GMT
• Title: Accidental Turntables: Learning 3D Pose by Watching Objects Turn
• Authors: Zezhou Cheng, Matheus Gadelha, Subhransu Maji
• Abstract summary: 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. 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.
• Score: 36.71437141704823
• License: http://creativecommons.org/licenses/by/4.0/
• 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, 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.

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