  The task of estimating the 6D pose of an object from RGB images can be broken
down into two main steps: an initial pose estimation step, followed by a
refinement procedure to correctly register the object and its observation. In
this paper, we propose a new method for 6D pose estimation refinement from RGB
images. To achieve high accuracy of the final estimate, the observation and a
rendered model need to be aligned. Our main insight is that after the initial
pose estimate, it is important to pay attention to distinct spatial features of
the object in order to improve the estimation accuracy during alignment.
Furthermore, parts of the object that are occluded in the image should be given
less weight during the alignment process. Most state-of-the-art refinement
approaches do not allow for this fine-grained reasoning and can not fully
leverage the structure of the problem. In contrast, we propose a novel neural
network architecture built around a spatial attention mechanism that identifies
and leverages information about spatial details during pose refinement. We
experimentally show that this approach learns to attend to salient spatial
features and learns to ignore occluded parts of the object, leading to better
pose estimation across datasets. We conduct experiments on standard benchmark
datasets for 6D pose estimation (LineMOD and Occlusion LineMOD) and outperform
previous state-of-the-art methods.
