  This work aims to estimate 6Dof (6D) object pose in background clutter.
Considering the strong occlusion and background noise, we propose to utilize
the spatial structure for better tackling this challenging task. Observing that
the 3D mesh can be naturally abstracted by a graph, we build the graph using 3D
points as vertices and mesh connections as edges. We construct the
corresponding mapping from 2D image features to 3D points for filling the graph
and fusion of the 2D and 3D features. Afterward, a Graph Convolutional Network
(GCN) is applied to help the feature exchange among objects' points in 3D
space. To address the problem of rotation symmetry ambiguity for objects, a
spherical convolution is utilized and the spherical features are combined with
the convolutional features that are mapped to the graph. Predefined 3D
keypoints are voted and the 6DoF pose is obtained via the fitting optimization.
Two scenarios of inference, one with the depth information and the other
without it are discussed. Tested on the datasets of YCB-Video and LINEMOD, the
experiments demonstrate the effectiveness of our proposed method.
