  Numerous point-cloud understanding techniques focus on whole entities and
have succeeded in obtaining satisfactory results and limited sparsity
tolerance. However, these methods are generally sensitive to incomplete point
clouds that are scanned with flaws or large gaps. To address this issue, in
this paper, we propose an end-to-end architecture that compensates for and
identifies partial point clouds on the fly. First, we propose a cascaded
solution that integrates both the upstream and downstream networks
simultaneously, allowing the task-oriented downstream to identify the points
generated by the completion-oriented upstream. These two streams complement
each other, resulting in improved performance for both completion and
downstream-dependent tasks. Second, to explicitly understand the predicted
points' pattern, we introduce hierarchical self-distillation (HSD), which can
be applied to arbitrary hierarchy-based point cloud methods. HSD ensures that
the deepest classifier with a larger perceptual field and longer code length
provides additional regularization to intermediate ones rather than simply
aggregating the multi-scale features, and therefore maximizing the mutual
information between a teacher and students. We show the advantage of the
self-distillation process in the hyperspaces based on the information
bottleneck principle. On the classification task, our proposed method performs
competitively on the synthetic dataset and achieves superior results on the
challenging real-world benchmark when compared to the state-of-the-art models.
Additional experiments also demonstrate the superior performance and generality
of our framework on the part segmentation task.
