Parts with high-quality freeform surfaces have been widely used in industries, which require
strict quality control during the manufacturing process. Among all the industrial inspection …

We present a novel method for reconstructing a 3D implicit surface from a large-scale,
sparse, and noisy point cloud. Our approach builds upon the recently introduced Neural …

As 3D point clouds become the representation of choice for multiple vision and graphics
applications, the ability to synthesize or reconstruct high-resolution, high-fidelity point clouds …

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 clouds are often sparse and incomplete. Existing shape completion methods are
incapable of generating details of objects or learning the complex point distributions. To this …

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 geometry processing algorithms use meshes as the default shape
representation. Manipulating meshes, however, requires one to maintain high quality in the …

We present a detail-driven deep neural network for point set upsampling. A high-resolution
point set is essential for point-based rendering and surface reconstruction. Inspired by the …

We study an unsupervised domain adaptation problem for the semantic labeling of 3D point
clouds, with a particular focus on domain discrepancies induced by different LiDAR sensors …