[1912.02805] KeyPose: Multi-View 3D Labeling and Keypoint Estimation for Transparent Objectsopen searchopen navigation menucontact arXivsubscribe to arXiv mailings

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 with an RGB camera; and second, we develop a deep neural network, called $KeyPose$, that learns to accurately predict object poses using 3D keypoints, from stereo input, and works even for transparent objects. To evaluate the performance of our method, we create a dataset of 15 clear objects in five classes, with 48K 3D-keypoint labeled images. We train both instance and category models, and show generalization to new textures, poses, and objects. KeyPose surpasses state-of-the-art performance in

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@norihitoishida KeyPose: Multi-View 3D Labeling and Keypoint Estimation for Transparent Objects (Liu+, CVPR 2020) t.co/O5BMBZ8UkH t.co/rYmE1u8fxP - 3D姿勢推定でDenseFusion超え - 深度情報非依存(ステレオRBGのみ) - 透明オブジェクトに強い - 今は半教師だけど自己教師でもっと効率↑

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