  Understanding the implication of point cloud is still challenging to achieve
the goal of classification or segmentation due to the irregular and sparse
structure of point cloud. As we have known, PointNet architecture as a
ground-breaking work for point cloud which can learn efficiently shape features
directly on unordered 3D point cloud and have achieved favorable performance.
However, this model fail to consider the fine-grained semantic information of
local structure for point cloud. Afterwards, many valuable works are proposed
to enhance the performance of PointNet by means of semantic features of local
patch for point cloud. In this paper, a multi-scale receptive fields graph
attention network (named after MRFGAT) for point cloud classification is
proposed. By focusing on the local fine features of point cloud and applying
multi attention modules based on channel affinity, the learned feature map for
our network can well capture the abundant features information of point cloud.
The proposed MRFGAT architecture is tested on ModelNet10 and ModelNet40
datasets, and results show it achieves state-of-the-art performance in shape
classification tasks.
