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arXiv · 2210.13540

Video based Object 6D Pose Estimation using Transformers

Apoorva Beedu·Huda Alamri·Irfan Essa
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Abstract

We introduce a Transformer based 6D Object Pose Estimation framework VideoPose, comprising an end-to-end attention based modelling architecture, that attends to previous frames in order to estimate accurate 6D Object Poses in videos. Our approach leverages the temporal information from a video sequence for pose refinement, along with being computationally efficient and robust. Compared to existing methods, our architecture is able to capture and reason from long-range dependencies efficiently, thus iteratively refining over video sequences. Experimental evaluation on the YCB-Video dataset shows that our approach is on par with the state-of-the-art Transformer methods, and performs significantly better relative to CNN based approaches. Further, with a speed of 33 fps, it is also more efficient and therefore applicable to a variety of applications that require real-time object pose estimation. Training code and pretrained models are available at https://github.com/ApoorvaBeedu/VideoPose

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BibTeXRIS

Apoorva Beedu, Huda Alamri, Irfan Essa. 2022-10-24. Video based Object 6D Pose Estimation using Transformers. https://arxiv.org/abs/2210.13540

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