  Following the tremendous success of transformer in natural language
processing and image understanding tasks, in this paper, we present a novel
point cloud representation learning architecture, named Dual Transformer
Network (DTNet), which mainly consists of Dual Point Cloud Transformer (DPCT)
module. Specifically, by aggregating the well-designed point-wise and
channel-wise multi-head self-attention models simultaneously, DPCT module can
capture much richer contextual dependencies semantically from the perspective
of position and channel. With the DPCT module as a fundamental component, we
construct the DTNet for performing point cloud analysis in an end-to-end
manner. Extensive quantitative and qualitative experiments on publicly
available benchmarks demonstrate the effectiveness of our proposed transformer
framework for the tasks of 3D point cloud classification and segmentation,
achieving highly competitive performance in comparison with the
state-of-the-art approaches.
