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3D Pose Estimation based on Deep Learning without Real World Data Training | IEEE Conference Publication | IEEE Xplore

3D Pose Estimation based on Deep Learning without Real World Data Training


Abstract:

This paper proposes a new methodology to carry out the 3D pose estimation of an object. We assume a single lens camera environment, and use a game engine to generate virt...Show More

Abstract:

This paper proposes a new methodology to carry out the 3D pose estimation of an object. We assume a single lens camera environment, and use a game engine to generate virtual world datasets in order to assist a deep learning model. Using only the virtual world datasets to conduct training has been tested with the real world data and the accuracy can be achieved up to 84.22% with 30M parameters.
Date of Conference: 28-30 September 2020
Date Added to IEEE Xplore: 23 November 2020
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Conference Location: Taoyuan, Taiwan

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