  Point cloud completion is the task of predicting complete geometry from
partial observations using a point set representation for a 3D shape. Previous
approaches propose neural networks to directly estimate the whole point cloud
through encoder-decoder models fed by the incomplete point set. By predicting
the complete model, the current methods compute redundant information because
the output also contains the known incomplete input geometry. This paper
proposes an end-to-end neural network architecture that focuses on computing
the missing geometry and merging the known input and the predicted point cloud.
Our method is composed of two neural networks: the missing part prediction
network and the merging-refinement network. The first module focuses on
extracting information from the incomplete input to infer the missing geometry.
The second module merges both point clouds and improves the distribution of the
points. Our experiments on ShapeNet dataset show that our method outperforms
the state-of-the-art methods in point cloud completion. The code of our methods
and experiments is available in
\url{https://github.com/ivansipiran/Refinement-Point-Cloud-Completion}.
