Dual Transformer for Point Cloud Analysis

• URL: http://arxiv.org/abs/2104.13044v1
• Date: Tue, 27 Apr 2021 08:41:02 GMT
• Title: Dual Transformer for Point Cloud Analysis
• Authors: Xian-Feng Han and Yi-Fei Jin and Hui-Xian Cheng and Guo-Qiang Xiao
• Abstract summary: We present a novel point cloud representation learning architecture, named Dual Transformer Network (DTNet) 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 perspective of position and channel. 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.
• Score: 2.160196691362033
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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