We introduce HybridPose, a novel 6D object pose estimation approach. HybridPose utilizes a hybrid intermediate representation to express different geometric information in the input image, including keypoints, edge vectors, and symmetry correspondences. Compared to a unitary representation, our hybrid representation allows pose regression to exploit more and diverse features when one type of predicted representation is inaccurate (e.g., because of occlusion). HybridPose leverages a robust regression module to filter out outliers in predicted intermediate representation. We show the robustness of Hy-bridPose by demonstrating that all intermediate representations can be predicted by the same simple neural network without sacrificing the overall performance. Compared to state-of-the-art pose estimation approaches, Hy-bridPose is comparable in running time and is significantly more accurate. For example, on Occlusion Linemod [3] dataset, our method achieves a prediction speed of 30 fps with a mean ADD(-S) accuracy of 79.2%, representing a 67.4% improvement from the current state-of-the-art approach. Our implementation of HybridPose is available at
REF utilizes a hybrid intermediate representation to provide more supervision during training.
HybridPose: 6D Object Pose Estimation under Hybrid Representations
ArXiv
ArXiv
Computer Science