  6D object pose estimation is one of the fundamental problems in computer
vision and robotics research. While a lot of recent efforts have been made on
generalizing pose estimation to novel object instances within the same
category, namely category-level 6D pose estimation, it is still restricted in
constrained environments given the limited number of annotated data. In this
paper, we collect Wild6D, a new unlabeled RGBD object video dataset with
diverse instances and backgrounds. We utilize this data to generalize
category-level 6D object pose estimation in the wild with semi-supervised
learning. We propose a new model, called Rendering for Pose estimation network
RePoNet, that is jointly trained using the free ground-truths with the
synthetic data, and a silhouette matching objective function on the real-world
data. Without using any 3D annotations on real data, our method outperforms
state-of-the-art methods on the previous dataset and our Wild6D test set (with
manual annotations for evaluation) by a large margin. Project page with Wild6D
data: https://oasisyang.github.io/semi-pose .
