论文标题
点变压器
Point Transformer
论文作者
论文摘要
在这项工作中，我们提出了Point Transformer，这是一个直接在无序和非结构化点集上运行的深神经网络。我们设计了点变压器来提取本地和全局特征，并通过引入局部全球注意机制来关联这两种表示，该机制旨在捕获空间点关系和形状信息。为此，我们建议将SortNet作为点变压器的一部分，该网络通过根据学习分数选择点来诱导输入置换不变性。 Point Transformer的输出是一个分类和置换不变的功能列表，可以直接将其纳入通用的计算机视觉应用程序中。我们评估了我们的标准分类和零件分割基准的方法，以证明与先前的工作相比表明竞争成果。代码可公开可用：https：//github.com/engelnico/point-transformer
In this work, we present Point Transformer, a deep neural network that operates directly on unordered and unstructured point sets. We design Point Transformer to extract local and global features and relate both representations by introducing the local-global attention mechanism, which aims to capture spatial point relations and shape information. For that purpose, we propose SortNet, as part of the Point Transformer, which induces input permutation invariance by selecting points based on a learned score. The output of Point Transformer is a sorted and permutation invariant feature list that can directly be incorporated into common computer vision applications. We evaluate our approach on standard classification and part segmentation benchmarks to demonstrate competitive results compared to the prior work. Code is publicly available at: https://github.com/engelnico/point-transformer
