OSSID: Online Self-Supervised Instance Detection by (and for) Pose Estimation

• URL: http://arxiv.org/abs/2201.07309v1
• Date: Tue, 18 Jan 2022 20:55:56 GMT
• Title: OSSID: Online Self-Supervised Instance Detection by (and for) Pose Estimation
• Authors: Qiao Gu, Brian Okorn, David Held
• Abstract summary: Real-time object pose estimation is necessary for many robot manipulation algorithms. We propose the OSSID framework, leveraging a slow zero-shot pose estimator to self-supervise the training of a fast detection algorithm. We show that this self-supervised training exceeds the performance of existing zero-shot detection methods.
• Score: 17.78557307620686
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-time object pose estimation is necessary for many robot manipulation algorithms. However, state-of-the-art methods for object pose estimation are trained for a specific set of objects; these methods thus need to be retrained to estimate the pose of each new object, often requiring tens of GPU-days of training for optimal performance. \revisef{In this paper, we propose the OSSID framework,} leveraging a slow zero-shot pose estimator to self-supervise the training of a fast detection algorithm. This fast detector can then be used to filter the input to the pose estimator, drastically improving its inference speed. We show that this self-supervised training exceeds the performance of existing zero-shot detection methods on two widely used object pose estimation and detection datasets, without requiring any human annotations. Further, we show that the resulting method for pose estimation has a significantly faster inference speed, due to the ability to filter out large parts of the image. Thus, our method for self-supervised online learning of a detector (trained using pseudo-labels from a slow pose estimator) leads to accurate pose estimation at real-time speeds, without requiring human annotations. Supplementary materials and code can be found at https://georgegu1997.github.io/OSSID/

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