Rapid Pose Label Generation through Sparse Representation of Unknown Objects

• URL: http://arxiv.org/abs/2011.03790v1
• Date: Sat, 7 Nov 2020 15:14:03 GMT
• Title: Rapid Pose Label Generation through Sparse Representation of Unknown Objects
• Authors: Rohan Pratap Singh, Mehdi Benallegue, Yusuke Yoshiyasu, Fumio Kanehiro
• Abstract summary: This work presents an approach for rapidly generating real-world, pose-annotated RGB-D data for unknown objects. We first source minimalistic labelings of an ordered set of arbitrarily chosen keypoints over a set of RGB-D videos. By solving an optimization problem, we combine these labels under a world frame to recover a sparse, keypoint-based representation of the object.
• Score: 7.32172860877574
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep Convolutional Neural Networks (CNNs) have been successfully deployed on robots for 6-DoF object pose estimation through visual perception. However, obtaining labeled data on a scale required for the supervised training of CNNs is a difficult task - exacerbated if the object is novel and a 3D model is unavailable. To this end, this work presents an approach for rapidly generating real-world, pose-annotated RGB-D data for unknown objects. Our method not only circumvents the need for a prior 3D object model (textured or otherwise) but also bypasses complicated setups of fiducial markers, turntables, and sensors. With the help of a human user, we first source minimalistic labelings of an ordered set of arbitrarily chosen keypoints over a set of RGB-D videos. Then, by solving an optimization problem, we combine these labels under a world frame to recover a sparse, keypoint-based representation of the object. The sparse representation leads to the development of a dense model and the pose labels for each image frame in the set of scenes. We show that the sparse model can also be efficiently used for scaling to a large number of new scenes. We demonstrate the practicality of the generated labeled dataset by training a pipeline for 6-DoF object pose estimation and a pixel-wise segmentation network.

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