KeyPose: Multi-view 3D Labeling and Keypoint Estimation for Transparent Objects

Estimating the 3D pose of desktop objects is crucial for applications such as robotic manipulation. Finding the depth of the object is an important part of this task, both for training and prediction, and is usually accomplished with a depth sensor or markers in a motion-capture system. For transparent or highly reflective objects, such methods are not feasible without impinging on the resultant image of the object. Hence, many existing methods restrict themselves to opaque, lambertian objects that give good returns from RGBD sensors. In this paper we address two problems: first, establish an easy method for capturing and labeling 3D keypoints on desktop objects with a stereo sensor (no special depth sensor required); and second, develop a deep method, called $KeyPose$, that learns to accurately predict 3D keypoints on objects, including challenging ones such as transparent objects. To showcase the performance of the method, we create and employ a dataset of 15 clear objects in 5 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 3D pose estimation on this dataset, sometimes by a wide margin, and even in cases where the competing method is provided with registered depth. We will release a public version of the data capture and labeling pipeline, the transparent object database, and the KeyPose training and evaluation code.