  Learning structures of 3D shapes is a fundamental problem in the field of
computer graphics and geometry processing. We present a simple yet
interpretable unsupervised method for learning a new structural representation
in the form of 3D structure points. The 3D structure points produced by our
method encode the shape structure intrinsically and exhibit semantic
consistency across all the shape instances with similar structures. This is a
challenging goal that has not fully been achieved by other methods.
Specifically, our method takes a 3D point cloud as input and encodes it as a
set of local features. The local features are then passed through a novel point
integration module to produce a set of 3D structure points. The chamfer
distance is used as reconstruction loss to ensure the structure points lie
close to the input point cloud. Extensive experiments have shown that our
method outperforms the state-of-the-art on the semantic shape correspondence
task and achieves comparable performance with the state-of-the-art on the
segmentation label transfer task. Moreover, the PCA based shape embedding built
upon consistent structure points demonstrates good performance in preserving
the shape structures. Code is available at
https://github.com/NolenChen/3DStructurePoints
