VideoPose: Estimating 6D object pose from videos
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Abstract (arXiv)
We introduce a simple yet effective algorithm that uses convolutional neural networks to directly estimate object poses from videos. Our approach leverages the temporal information from a video sequence, and is computationally efficient and robust to support robotic and AR domains. Our proposed network takes a pre-trained 2D object detector as input, and aggregates visual features through a recurrent neural network to make predictions at each frame. Experimental evaluation on the YCB-Video dataset show that our approach is on par with the state-of-the-art algorithms. Further, with a speed of 30 fps, it is also more efficient than the state-of-the-art, and therefore applicable to a variety of applications that require real-time object pose estimation.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 31 of its 37 references that are in the map. First posted on arXiv 20 Nov 2021.
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31 of 37 references.
References · 31
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19 more
- 2018Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objectscited by 241
- 2017BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depthcited by 225
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- 2017T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objectscited by 210
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- 2019PoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Trackingcited by 91
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- 2018Real-Time 6D Object Pose Estimation on CPUcited by 7
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