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KeyPose: Multi-View 3D Labeling and Keypoint Estimation for Transparent Objects
[article]
2020
arXiv
pre-print
Estimating the 3D pose of desktop objects is crucial for applications such as robotic manipulation. Many existing approaches to this problem require a depth map of the object for both training and prediction, which restricts them to opaque, lambertian objects that produce good returns in an RGBD sensor. In this paper we forgo using a depth sensor in favor of raw stereo input. We address two problems: first, we establish an easy method for capturing and labeling 3D keypoints on desktop objects
arXiv:1912.02805v2
fatcat:ocknxyfefrcr5dfvh7nq3pdhsy