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Conference Papers Year : 2022

NEMA: 6-DoF Pose Estimation Dataset for Deep Learning


Maintenance is inevitable, time-consuming, expensive, and risky to production and maintenance operators. Porting maintenance support applications to mixed reality (MR) headsets would ease operations. To function, the application needs to anchor 3D graphics onto real objects, i.e. locate and track real-world objects in three dimensions. This task is known in the computer vision community as Six Degree of Freedom Pose Estimation (6-Dof) and is best solved using Convolutional Neural Networks (CNNs). Training them required numerous examples, but acquiring real labeled images for 6-DoF pose estimation is a challenge on its own. In this article, we propose first a thorough review of existing non-synthetic datasets for 6-DoF pose estimations. This allows identifying several reasons why synthetic training data has been favored over real training data. Nothing can replace real images. We show next that it is possible to overcome the limitations faced by previous datasets by presenting a new methodology for labeled images acquisition. And finally, we present a new dataset named NEMA that allows deep learning methods to be trained without the need for synthetic data.

Dates and versions

hal-03611407 , version 1 (17-03-2022)


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Philippe Pérez de San Roman, Pascal Desbarats, Jean-Philippe Domenger, Axel Buendia. NEMA: 6-DoF Pose Estimation Dataset for Deep Learning. 17th International Conference on Computer Vision Theory and Applications, Feb 2022, Online Streaming, France. pp.682-690, ⟨10.5220/0010913200003124⟩. ⟨hal-03611407⟩
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