Single Shot 6D Object Pose Estimation

• URL: http://arxiv.org/abs/2004.12729v1
• Date: Mon, 27 Apr 2020 11:59:11 GMT
• Title: Single Shot 6D Object Pose Estimation
• Authors: Kilian Kleeberger and Marco F. Huber
• Abstract summary: We introduce a novel single shot approach for 6D object pose estimation of rigid objects based on depth images. A fully convolutional neural network is employed, where the 3D input data is spatially discretized and pose estimation is considered as a regression task. With 65 fps on a GPU, our Object Pose Network (OP-Net) is extremely fast, is optimized end-to-end, and estimates the 6D pose of multiple objects in the image simultaneously.
• Score: 11.37625512264302
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we introduce a novel single shot approach for 6D object pose estimation of rigid objects based on depth images. For this purpose, a fully convolutional neural network is employed, where the 3D input data is spatially discretized and pose estimation is considered as a regression task that is solved locally on the resulting volume elements. With 65 fps on a GPU, our Object Pose Network (OP-Net) is extremely fast, is optimized end-to-end, and estimates the 6D pose of multiple objects in the image simultaneously. Our approach does not require manually 6D pose-annotated real-world datasets and transfers to the real world, although being entirely trained on synthetic data. The proposed method is evaluated on public benchmark datasets, where we can demonstrate that state-of-the-art methods are significantly outperformed.

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