  Recently, Transformer-based methods for point cloud learning have achieved
good results on various point cloud learning benchmarks. However, since the
attention mechanism needs to generate three feature vectors of query, key, and
value to calculate attention features, most of the existing Transformer-based
point cloud learning methods usually consume a large amount of computational
time and memory resources when calculating global attention. To address this
problem, we propose a Voxel-Transformer-Point (VTP) Block for extracting local
and global features of point clouds. VTP combines the advantages of
voxel-based, point-based and Transformer-based methods, which consists of
Voxel-Based Branch (V branch), Point-Based Transformer Branch (PT branch) and
Point-Based Branch (P branch). The V branch extracts the coarse-grained
features of the point cloud through low voxel resolution; the PT branch obtains
the fine-grained features of the point cloud by calculating the self-attention
in the local neighborhood and the inter-neighborhood cross-attention; the P
branch uses a simplified MLP network to generate the global location
information of the point cloud. In addition, to enrich the local features of
point clouds at different scales, we set the voxel scale in the V branch and
the neighborhood sphere scale in the PT branch to one large and one small
(large voxel scale \& small neighborhood sphere scale or small voxel scale \&
large neighborhood sphere scale). Finally, we use VTP as the feature extraction
network to construct a VTPNet for point cloud learning, and performs shape
classification, part segmentation, and semantic segmentation tasks on the
ModelNet40, ShapeNet Part, and S3DIS datasets. The experimental results
indicate that VTPNet has good performance in 3D point cloud learning.
