Recent supervised point cloud upsampling methods are restricted by the size of training data and are limited in terms of covering all object shapes. Besides the challenges faced due to data acquisition, the networks also struggle to generalize on unseen records. In this paper, we present an internal point cloud upsampling approach at a holistic level referred to as "Zero-Shot" Point Cloud Upsampling (ZSPU). Our approach is data agnostic and relies solely on the internal information provided by a particular point cloud without patching in both self-training and testing phases. This single-stream design significantly reduces the training time by learning the relation between low resolution (LR) point clouds and their high (original) resolution (HR) counterparts. This association will then provide super resolution (SR) outputs when original point clouds are loaded as input. ZSPU achieves competitive/superior quantitative and qualitative performances on benchmark datasets when compared with other upsampling methods.

翻译：最近受监督的云层上取样方法受培训数据规模的限制,而且覆盖所有对象形状的方法也有限。除了由于数据获取而面临的挑战之外,网络还难以对不可见的记录进行概括化。在本文中,我们提出了一个整体层面的内部点云上取样方法,称为“零热”点云上取样法(ZSPU)。我们的方法是数据不可知性,完全依赖特定点云提供的内部信息,而不在自我培训和测试阶段进行补足。这种单流设计通过学习低分辨率(LR)点云与其高(原)分辨率(HR)对应方之间的关系,大大减少了培训时间。当原始点云作为投入被装入时,这种关联将提供超分辨率(SR)输出。在与其他抽样方法相比,ZSPU在基准数据集上实现竞争性/超强定量和定性业绩。