GP3D: Generalized Pose Estimation in 3D Point Clouds: A case study on bin picking

In this paper, we present GP3D, a novel network for generalized pose estimation in 3D point clouds. The method generalizes to new objects by using both the scene point cloud and the object point cloud with keypoint indexes as input. The network is trained to match the object keypoints to scene points. To address the pose estimation of novel objects we also present a new approach for training pose estimation. The typical solution is a single model trained for pose estimation of a specific object in any scenario. This has several drawbacks: training a model for each object is time-consuming, energy consuming, and by excluding the scenario information the task becomes more difficult. In this paper, we present the opposite solution; a scenario-specific pose estimation method for novel objects that do not require retraining. The network is trained on 1500 objects and is able to learn a generalized solution. We demonstrate that the network is able to correctly predict novel objects, and demonstrate the ability of the network to perform outside of the trained class. We believe that the demonstrated method is a valuable solution for many real-world scenarios. Code and trained network will be made available after publication.