  Semantic 3D keypoints are category-level semantic consistent points on 3D
objects. Detecting 3D semantic keypoints is a foundation for a number of 3D
vision tasks but remains challenging, due to the ambiguity of semantic
information, especially when the objects are represented by unordered 3D point
clouds. Existing unsupervised methods tend to generate category-level keypoints
in implicit manners, making it difficult to extract high-level information,
such as semantic labels and topology. From a novel mutual reconstruction
perspective, we present an unsupervised method to generate consistent semantic
keypoints from point clouds explicitly. To achieve this, the proposed model
predicts keypoints that not only reconstruct the object itself but also
reconstruct other instances in the same category. To the best of our knowledge,
the proposed method is the first to mine 3D semantic consistent keypoints from
a mutual reconstruction view. Experiments under various evaluation metrics as
well as comparisons with the state-of-the-arts demonstrate the efficacy of our
new solution to mining semantic consistent keypoints with mutual
reconstruction.
