"Zero-Shot" Point Cloud Upsampling
This addresses the limitation of supervised methods in generalizing to unseen shapes, offering a more flexible solution for 3D vision applications.
The paper tackles the problem of point cloud upsampling by introducing a data-agnostic, zero-shot method that uses internal information from point clouds without patching, achieving competitive or superior performance on benchmark datasets.
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.