Face Recognition For Fisheye Images

We present a model for category-agnostic 6D object pose tracking. We tackle object pose tracking as a 3D keypoint detection and matching task that does not require ground-truth annotation of the keypoints. Using RGB-D data and the target object mask as inputs, we spatially segment the point cloud of the object into clusters. Each 3D point in the cluster is characterised by features encoding appearance and geometric information. We use these features to detect a keypoint for each cluster, and obtain a keypoint set of the object. With the detected keypoint sets from two frames, the inter-frame pose change is recovered through least-squares optimisation. The loss functions are designed to ensure that the detected keypoints are consistent in two frames and suitable for pose tracking.