  Owing to the development of research on local aggregation operators, dramatic
breakthrough has been made in point cloud analysis models. However, existing
local aggregation operators in the current literature fail to attach decent
importance to the local information of the point cloud, which limits the power
of the models. To fit this gap, we propose an efficient Vector Attention
Convolution module (VAConv), which utilizes K-Nearest Neighbor (KNN) to extract
the neighbor points of each input point, and then uses the elevation and
azimuth relationship of the vectors between the center point and its neighbors
to construct an attention weight matrix for edge features. Afterwards, the
VAConv adopts a dual-channel structure to fuse weighted edge features and
global features. To verify the efficiency of the VAConv, we connect the VAConvs
with different receptive fields in parallel to obtain a Multi-scale graph
convolutional network, VA-GCN. The proposed VA-GCN achieves state-of-the-art
performance on standard benchmarks including ModelNet40, S3DIS and ShapeNet.
Remarkably, on the ModelNet40 dataset for 3D classification, VA-GCN increased
by 2.4% compared to the baseline.

Computer Vision and Pattern Recognition