In this paper, we propose a novel 3D graph convolution based pipeline for category-level 6D pose and size estimation from monocular RGB-D images. The proposed method leverages an efficient 3D data augmentation and a novel vector-based decoupled rotation representation. Specifically, we first design an orientation-aware autoencoder with 3D graph convolution for latent feature learning. The learned latent feature is insensitive to point shift and size thanks to the shift and scale-invariance properties of the 3D graph convolution. Then, to efficiently decode the rotation information from the latent feature, we design a novel flexible vector-based decomposable rotation representation that employs two decoders to complementarily access the rotation information. The proposed rotation representation has two major advantages: 1) decoupled characteristic that makes the rotation estimation easier; 2) flexible length and rotated angle of the vectors allow us to find a more suitable vector representation for specific pose estimation task. Finally, we propose a 3D deformation mechanism to increase the generalization ability of the pipeline. Extensive experiments show that the proposed pipeline achieves state-of-the-art performance on category-level tasks. Further, the experiments demonstrate that the proposed rotation representation is more suitable for the pose estimation tasks than other rotation representations.

翻译：在本文中,我们提出一个新的 3D 图形化6D 配置和尺寸估计管道的3D 图形化配置,用于 6D 配置和以单向 RGB-D 图像进行尺寸估计。拟议方法利用了高效的 3D 数据扩增和基于病媒的分解旋转代表制。具体地说,我们首先设计了方向-觉变自动编码器和3D 图形化组合,用于潜在的特征学习。由于 3D 图形化的变换和规模变化特性,所学的潜伏特征对于点变换和大小不敏感。然后,为了有效地将旋转信息从潜在特征中解码,我们设计了一个新的灵活的基于病媒的可复变轮换代表制,使用两个解码器来补充轮换信息。拟议的轮换代表制有两个主要优势:(1) 使旋转估计更容易被拆分解的特性;(2) 灵活的矢量和旋转角度使我们能找到更合适的矢量代表制代表制,用于具体的估测度任务。最后,我们提议了一个3D 变变变机制,以提高管道的普及能力。广泛的实验显示,拟议的管道实现基于状态的分解式调整的状态,而不是在类别一级上进行适当的演化,进一步显示其他任务。