Point cloud data have been widely explored due to its superior accuracy and robustness
under various adverse situations. Meanwhile, deep neural networks (DNNs) have achieved …

Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …

We propose a generative adversarial network for point cloud upsampling, which can not
only make the upsampled points evenly distributed on the underlying surface but also …

Point clouds produced by 3D scanning are often sparse, non-uniform, and noisy. Recent
upsampling approaches aim to generate a dense point set, while achieving both distribution …

Most existing point cloud upsampling methods have roughly three steps: feature extraction,
feature expansion and 3D coordinate prediction. However, they usually suffer from two …

The effectiveness of learning-based point cloud upsampling pipelines heavily relies on the
upsampling modules and feature extractors used therein. For the point upsampling module …

Given the rapid development of 3D scanners, point clouds are becoming popular in AI-
driven machines. However, point cloud data is inherently sparse and irregular, causing …

We propose a novel method that renders point clouds as if they are surfaces. The proposed
method is differentiable and requires no scene-specific optimization. This unique capability …

In this paper, we propose Neural Points, a novel point cloud representation and apply it to
the arbitrary-factored upsampling task. Different from traditional point cloud representation …

Recently, many deep neural networks were designed to process 3D point clouds, but a
common drawback is that rotation invariance is not ensured, leading to poor generalization …