  6D pose estimation refers to object recognition and estimation of 3D rotation
and 3D translation. The key technology for estimating 6D pose is to estimate
pose by extracting enough features to find pose in any environment. Previous
methods utilized depth information in the refinement process or were designed
as a heterogeneous architecture for each data space to extract feature.
However, these methods are limited in that they cannot extract sufficient
feature. Therefore, this paper proposes a Point Attention Module that can
efficiently extract powerful feature from RGB-D. In our Module, attention map
is formed through a Geometric Attention Path(GAP) and Channel Attention
Path(CAP). In GAP, it is designed to pay attention to important information in
geometric information, and CAP is designed to pay attention to important
information in Channel information. We show that the attention module
efficiently creates feature representations without significantly increasing
computational complexity. Experimental results show that the proposed method
outperforms the existing methods in benchmarks, YCB Video and LineMod. In
addition, the attention module was applied to the classification task, and it
was confirmed that the performance significantly improved compared to the
existing model.
