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.

中文翻译：

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用于点云分析的双变压器

继变压器在自然语言处理和图像理解任务中取得巨大成功之后，本文提出了一种新颖的点云表示学习体系结构，称为双变压器网络（DTNet），它主要由双点云变压器（DPCT）模块组成。具体而言，通过同时汇总精心设计的点向和通道多头自注意力模型，DPCT模块可以从位置和通道的角度语义上捕获更丰富的上下文相关性。以DPCT模块为基本组件，我们构建了DTNet，以便以端到端的方式执行点云分析。