论文标题
重建意识到半监督点云完成的事先蒸馏
Reconstruction-Aware Prior Distillation for Semi-supervised Point Cloud Completion
论文作者
论文摘要
现实世界中的传感器通常会产生不完整，不规则和嘈杂的点云，从而使点云的完成越来越重要。但是，大多数现有的完成方法都依靠大型配对数据集进行培训，这是劳动密集型的。本文提出了RAPD，这是一种新型的半监督点云完成方法，可减少对配对数据集的需求。 RAPD利用了两阶段的训练方案，在第1阶段中，从未配对的完整和不完整的点云中学到了深层的语义先验，并且在第2阶段中引入了半监督的先前蒸馏过程，以仅使用少量配对样品来训练一个完成网络。此外，引入了一个自我监督的完成模块，以使用未配对的不完整点云来提高性能。多个数据集上的实验表明，RAPD在同源和异源方案中都优于先前的方法。
Real-world sensors often produce incomplete, irregular, and noisy point clouds, making point cloud completion increasingly important. However, most existing completion methods rely on large paired datasets for training, which is labor-intensive. This paper proposes RaPD, a novel semi-supervised point cloud completion method that reduces the need for paired datasets. RaPD utilizes a two-stage training scheme, where a deep semantic prior is learned in stage 1 from unpaired complete and incomplete point clouds, and a semi-supervised prior distillation process is introduced in stage 2 to train a completion network using only a small number of paired samples. Additionally, a self-supervised completion module is introduced to improve performance using unpaired incomplete point clouds. Experiments on multiple datasets show that RaPD outperforms previous methods in both homologous and heterologous scenarios.
