  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.
