Estimating the 6D object pose is an essential task in many applications. Due to the lack of depth information, existing RGB-based methods are sensitive to occlusion and illumination changes. How to extract and utilize the geometry features in depth information is crucial to achieve accurate predictions. To this end, we propose TransPose, a novel 6D pose framework that exploits Transformer Encoder with geometry-aware module to develop better learning of point cloud feature representations. Specifically, we first uniformly sample point cloud and extract local geometry features with the designed local feature extractor base on graph convolution network. To improve robustness to occlusion, we adopt Transformer to perform the exchange of global information, making each local feature contains global information. Finally, we introduce geometry-aware module in Transformer Encoder, which to form an effective constrain for point cloud feature learning and makes the global information exchange more tightly coupled with point cloud tasks. Extensive experiments indicate the effectiveness of TransPose, our pose estimation pipeline achieves competitive results on three benchmark datasets.

TransPose是一个利用Transformer编码器和几何感知模块的6D姿态框架，用于提取和利用深度信息中的几何特征，以实现更准确的预测。通过统一采样点云，并借助设计的局部特征提取器和图卷积网络提取局部几何特征；采用Transformer进行全局信息交换，使每个局部特征包含全局信息；最后，在Transformer编码器中引入几何感知模块，为点云特征学习提供有效约束，并使全局信息交换与点云任务更加紧密结合。实验结果表明，通过TransPose的6D姿态估计管道在三个基准数据集上取得了有竞争力的结果。

TransPose：基于几何感知的Transformer的6D物体姿态估计