  Since the PointNet was proposed, deep learning on point cloud has been the
concentration of intense 3D research. However, existing point-based methods
usually are not adequate to extract the local features and the spatial pattern
of a point cloud for further shape understanding. This paper presents an
end-to-end framework, SK-Net, to jointly optimize the inference of spatial
keypoint with the learning of feature representation of a point cloud for a
specific point cloud task. One key process of SK-Net is the generation of
spatial keypoints (Skeypoints). It is jointly conducted by two proposed
regulating losses and a task objective function without knowledge of Skeypoint
location annotations and proposals. Specifically, our Skeypoints are not
sensitive to the location consistency but are acutely aware of shape. Another
key process of SK-Net is the extraction of the local structure of Skeypoints
(detail feature) and the local spatial pattern of normalized Skeypoints
(pattern feature). This process generates a comprehensive representation,
pattern-detail (PD) feature, which comprises the local detail information of a
point cloud and reveals its spatial pattern through the part district
reconstruction on normalized Skeypoints. Consequently, our network is prompted
to effectively understand the correlation between different regions of a point
cloud and integrate contextual information of the point cloud. In point cloud
tasks, such as classification and segmentation, our proposed method performs
better than or comparable with the state-of-the-art approaches. We also present
an ablation study to demonstrate the advantages of SK-Net.
