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
翻译:在这项工作中,我们提出点变换器,这是一个直接在无定序和无结构的点组合上运行的深层神经网络;我们设计点变换器,以提取地方和全球特点,并通过引入旨在捕捉空间点关系和形成信息的地方-全球关注机制,将两者联系起来;为此,我们提议SortNet,作为点变换器的一部分,通过根据学到的分数选择点,引起输入变异。点变换器的输出是可直接纳入共同计算机视野应用的分类和变异特性列表。我们评价我们的标准分类和部分分割基准方法,以显示与先前工作相比的竞争结果。