  Although accurate and fast point cloud classification is a fundamental task
in 3D applications, it is difficult to achieve this purpose due to the
irregularity and disorder of point clouds that make it challenging to achieve
effective and efficient global discriminative feature learning. Lately, 3D
Transformers have been adopted to improve point cloud processing. Nevertheless,
massive Transformer layers tend to incur huge computational and memory costs.
This paper presents a novel hierarchical framework that incorporates
convolution with Transformer for point cloud classification, named 3D
Convolution-Transformer Network (3DCTN), to combine the strong and efficient
local feature learning ability of convolution with the remarkable global
context modeling capability of Transformer. Our method has two main modules
operating on the downsampling point sets, and each module consists of a
multi-scale local feature aggregating (LFA) block and a global feature learning
(GFL) block, which are implemented by using Graph Convolution and Transformer
respectively. We also conduct a detailed investigation on a series of
Transformer variants to explore better performance for our network. Various
experiments on ModelNet40 demonstrate that our method achieves state-of-the-art
classification performance, in terms of both accuracy and efficiency.
