Video based Object 6D Pose Estimation using Transformers

• URL: http://arxiv.org/abs/2210.13540v1
• Date: Mon, 24 Oct 2022 18:45:53 GMT
• Title: Video based Object 6D Pose Estimation using Transformers
• Authors: Apoorva Beedu, Huda Alamri, Irfan Essa
• Abstract summary: VideoPose is an end-to-end attention based modelling architecture that attends to previous frames in order to estimate 6D Object Poses in videos. Our architecture is able to capture and reason from long-range dependencies efficiently, thus iteratively refining over video sequences. Our approach is on par with the state-of-the-art Transformer methods, and performs significantly better relative to CNN based approaches.
• Score: 6.951360830202521
• License: http://creativecommons.org/licenses/by/4.0/
• 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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