LocalPose: Object Pose Estimation with Local Geometry Guidance

We present LocalPose, a novel method for 9 DoF object pose estimation from object point clouds. Existing works regress pose directly from the global shape embedding and are limited to a fixed set of shapes. We identify that the global object pose is closely related to local geometry properties like surface orientations at representative regions. Therefore, our key idea is to summarize local geometry properties as pose signatures at each point and aggregate them into the global pose, where local pose signatures are easier to learn by the network and generalize to novel shapes. We find two types of pose signatures that benefit pose estimation. First, we learn a neural network to predict 9 DoF pose signatures as pose candidates, and the process of voting them for the object pose. Second, we treat surface normals as direct pose regulators who help to select a subset of pose candidates to achieve the best accuracy. Experiments show that our method outperforms the state-of-the-art in terms of fine-grained pose accuracy on synthetic and real datasets, contributed by both pose signatures as candidates and regulators.