Domain Transfer for 3D Pose Estimation from Color Images without Manual Annotations

Mahdi Rad, Markus Oberweger, Vincent Lepetit

Research output: Contribution to journalArticleResearchpeer-review

Abstract

We introduce a novel learning method for 3D pose estimation from color images. While acquiring annotations for color images is a difficult task, our approach circumvents this problem by learning a mapping from paired color and depth images captured with an RGB-D camera. We jointly learn the pose from synthetic depth images that are easy to generate, and learn to align these synthetic depth images with the real depth images. We show our approach for the task of 3D hand pose estimation and 3D object pose estimation, both from color images only. Our method achieves performances comparable to state-of-the-art methods on popular benchmark datasets, without requiring any annotations for the color images.
Original languageEnglish
Number of pages16
JournalarXiv.org e-Print archive
Publication statusPublished - 8 Oct 2018
Event14th Asian Conference on Computer Vision - Perth Western Australia, Perth, Australia
Duration: 4 Dec 20186 Dec 2018
http://accv2018.net

Keywords

  • cs.CV

Cite this

Domain Transfer for 3D Pose Estimation from Color Images without Manual Annotations. / Rad, Mahdi; Oberweger, Markus; Lepetit, Vincent.

In: arXiv.org e-Print archive, 08.10.2018.

Research output: Contribution to journalArticleResearchpeer-review

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AB - We introduce a novel learning method for 3D pose estimation from color images. While acquiring annotations for color images is a difficult task, our approach circumvents this problem by learning a mapping from paired color and depth images captured with an RGB-D camera. We jointly learn the pose from synthetic depth images that are easy to generate, and learn to align these synthetic depth images with the real depth images. We show our approach for the task of 3D hand pose estimation and 3D object pose estimation, both from color images only. Our method achieves performances comparable to state-of-the-art methods on popular benchmark datasets, without requiring any annotations for the color images.

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