With the development of 3D scanning technologies, 3D vision tasks have become
a popular research area. Owing to the large amount of data acquired by sensors,
unsupervised learning is essential for understanding and utilizing point clouds
without an expensive annotation process. In this paper, we propose a novel
framework and an effective auto-encoder architecture named "PSG-Net" for
reconstruction-based learning of point clouds. Unlike existing studies that
used fixed or random 2D points, our framework generates input-dependent
point-wise features for the latent point set. PSG-Net uses the encoded input to
produce point-wise features through the seed generation module and extracts
richer features in multiple stages with gradually increasing resolution by
applying the seed feature propagation module progressively. We prove the
effectiveness of PSG-Net experimentally; PSG-Net shows state-of-the-art
performances in point cloud reconstruction and unsupervised classification, and
achieves comparable performance to counterpart methods in supervised
completion.

本文提出了一种基于点云重建的学习模型 PSG-Net，它使用了新的自编码器结构，并通过无监督学习实现对点云的重建和分类，优化了传统的鉴别模型，取得了显著的效果。