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.

本文提出了一种端到端的架构，用于处理和识别部分点云，具有层级自蒸馏和在部分分割任务上具有卓越性能的优势。

分层自蒸馏下的散点云理解的联合学习