Learning and selecting important points on a point cloud is crucial for point cloud understanding in various applications. Most of early methods selected the important points on 3D shapes by analyzing the intrinsic geometric properties of every single shape, which fails to capture the importance of points that distinguishes a shape from objects of other classes, i.e., the distinction of points. To address this problem, we propose D-Net (Distinctive Network) to learn for distinctive point clouds based on a self-attentive point searching and a learnable feature fusion. Specifically, in the self-attentive point searching, we first learn the distinction score for each point to reveal the distinction distribution of the point cloud. After ranking the learned distinction scores, we group a point cloud into a high distinctive point set and a low distinctive one to enrich the fine-grained point cloud structure. To generate a compact feature representation for each distinctive point set, a stacked self-gated convolution is proposed to extract the distinctive features. Finally, we further introduce a learnable feature fusion mechanism to aggregate multiple distinctive features into a global point cloud representation in a channel-wise aggregation manner. The results also show that the learned distinction distribution of a point cloud is highly consistent with objects of the same class and different from objects of other classes. Extensive experiments on public datasets, including ModelNet and ShapeNet part dataset, demonstrate the ability to learn for distinctive point clouds, which helps to achieve the state-of-the-art performance in some shape understanding applications.

翻译：在多种应用中，学习和选择点云上的重要点对于点云理解至关重要。早期方法大多通过分析每个形状的内在几何属性来选择三维形状上的重要点，但未能捕捉到能够区分某类形状与其他类物体的点的显著性，即点的区分性。为解决这一问题，我们提出D-Net（差异化网络），基于自注意力点搜索和可学习特征融合来学习差异化点云。具体而言，在自注意力点搜索中，我们首先为每个点学习区分性分数，以揭示点云的区分性分布。对学习到的区分性分数进行排序后，我们将点云分为高区分性点集和低区分性点集，以丰富细粒度点云结构。为生成每个区分性点集的紧凑特征表示，我们提出堆叠自门控卷积来提取区分性特征。最后，我们进一步引入可学习特征融合机制，以通道聚合的方式将多个区分性特征融合为全局点云表示。结果还表明，点云学习到的区分性分布与同类物体高度一致，而与其他类物体不同。在ModelNet和ShapeNet部件数据集等公开数据集上的大量实验证明，该方法具备学习差异化点云的能力，有助于在部分形状理解应用中实现最先进的性能。