Semi-auto labeling device for 6Dof object pose estimation

Meizhen Liao; Wei Wei; Xiaojie Zhang; Yuzhong Long; Hanxi Li

doi:10.1117/12.2636942

29 April 2022 Semi-auto labeling device for 6Dof object pose estimation

Meizhen Liao, Wei Wei, Xiaojie Zhang, Yuzhong Long, Hanxi Li

Proceedings Volume 12247, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2022); 122471D (2022) https://doi.org/10.1117/12.2636942
Event: 2022 International Conference on Image, Signal Processing, and Pattern Recognition, 2022, Guilin, China

Abstract

In this work, we propose to generate the supervised training dataset for 6-DoF object pose estimation algorithm, with trivial human labor. A semi-auto labelling board is designed so that its pose can be estimated accurately with a learned deep model. For a training sequence with more than 1000 frames, one only needs to manually mark the key points of the object for 2-5 frames and the ground-truth pose of the object can be calculated automatically for the whole sequence. In the experiment, we prove that the state-of-the-art pose estimation method can be trained well with only 20-50 human-labelled images and the yielded model performs better than the model learned based on the manually labelled dataset with more than 800 images.

Citation Download Citation

Meizhen Liao, Wei Wei, Xiaojie Zhang, Yuzhong Long, and Hanxi Li "Semi-auto labeling device for 6Dof object pose estimation", Proc. SPIE 12247, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2022), 122471D (29 April 2022); https://doi.org/10.1117/12.2636942

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