论文标题
杂交：在混合表示下的6D对象姿势估计
HybridPose: 6D Object Pose Estimation under Hybrid Representations
论文作者
论文摘要
我们引入了杂化，这是一种新型的6D对象姿势估计方法。杂交利用杂交中间表示在输入图像中表达不同的几何信息，包括关键点，边缘向量和对称对应。与单一表示相比，当一种类型的预测表示形式不准确时（例如，由于遮挡），我们的混合表示允许姿势回归更加多样化。杂交使用的不同中间表示都可以通过相同的简单神经网络预测，并且预测的中间表示中的异常值通过强大的回归模块过滤。与最先进的姿势估计方法相比，杂交时间和准确性相当。例如，在遮挡LineMod数据集上，我们的方法达到了30 fps的预测速度，平均添加（-s）精度为47.5％，代表最先进的性能。杂交的实施可在https://github.com/chensong1995/hybridpose上获得。
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). Different intermediate representations used by HybridPose can all be predicted by the same simple neural network, and outliers in predicted intermediate representations are filtered by a robust regression module. Compared to state-of-the-art pose estimation approaches, HybridPose is comparable in running time and accuracy. For example, on Occlusion Linemod dataset, our method achieves a prediction speed of 30 fps with a mean ADD(-S) accuracy of 47.5%, representing a state-of-the-art performance. The implementation of HybridPose is available at https://github.com/chensong1995/HybridPose.
