  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.
