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 …

We present a method to super-resolve voxelized point clouds downsampled by a fractional
factor, using lookup-tables (LUT) constructed from self-similarities from their own …

We propose a novel framework to reconstruct super-resolution human shape from a single
low-resolution input image. The approach overcomes limitations of existing approaches that …

Volumetric video is fully three-dimensional and immersive, which shows great potential in
various application scenarios. However, the naive delivery of volumetric video over the …

Graph signal processing is a ubiquitous task in many applications such as sensor, social,
transportation and brain networks, point cloud processing, and graph neural networks …

Point cloud (PC)—a collection of discrete geometric samples of a 3D object's surface—is
typically large, which entails expensive subsequent operations. Thus, PC sub-sampling is of …

Point cloud (PC) is a collection of discrete geometric samples of a physical object in 3D
space. A PC video consists of temporal frames evenly spaced in time, each containing a …

Dense colored point clouds enhance visual perception and are of significant value in
various robotic applications. However, existing learning-based point cloud upsampling …

We present PU-Refiner, a generative adversarial network for point cloud upsampling. The
generator of our network includes a coarse feature expansion module to create coarse …

We present PU-Mask, a virtual mask-based network for 3D point cloud upsampling. Unlike
existing upsampling methods, which treat point cloud upsampling as an “unconstrained …