In recent years, 3D point cloud has gained increasing attention as a new representation for
objects. However, the raw point cloud is often noisy and contains outliers. Therefore, it is …

The area of surface reconstruction has seen substantial progress in the past two decades.
The traditional problem addressed by surface reconstruction is to recover the digital …

Point clouds acquired from scanning devices are often perturbed by noise, which affects
downstream tasks such as surface reconstruction and analysis. The distribution of a noisy …

Point clouds acquired from range scans are often sparse, noisy, and non-uniform. This
paper presents a new point cloud upsampling network called PU-GAN, which is formulated …

Point cloud completion concerns to predict missing part for incomplete 3D shapes. A
common strategy is to generate complete shape according to incomplete input. However …

Learning and analyzing 3D point clouds with deep networks is challenging due to the
sparseness and irregularity of the data. In this paper, we present a data-driven point cloud …

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 …

In this paper, we introduce Point2Mesh, a technique for reconstructing a surface mesh from
an input point cloud. Instead of explicitly specifying a prior that encodes the expected shape …

Point clouds obtained with 3D scanners or by image‐based reconstruction techniques are
often corrupted with significant amount of noise and outliers. Traditional methods for point …