  Deep learning within the context of point clouds has gained much research
interest in recent years mostly due to the promising results that have been
achieved on a number of challenging benchmarks, such as 3D shape recognition
and scene semantic segmentation. In many realistic settings however, snapshots
of the environment are often taken from a single view, which only contains a
partial set of the scene due to the field of view restriction of commodity
cameras. 3D scene semantic understanding on partial point clouds is considered
as a challenging task. In this work, we propose a processing approach for 3D
point cloud data based on a multiview representation of the existing 360{\deg}
point clouds. By fusing the original 360{\deg} point clouds and their
corresponding 3D multiview representations as input data, a neural network is
able to recognize partial point sets while improving the general performance on
complete point sets, resulting in an overall increase of 31.9% and 4.3% in
segmentation accuracy for partial and complete scene semantic understanding,
respectively. This method can also be applied in a wider 3D recognition context
such as 3D part segmentation.
