Point cloud completion aims to recover the complete shape based on a partial observation.
Existing methods require either complete point clouds or multiple partial observations of the …

Point cloud completion is the task of producing a complete 3D shape given an input of a
partial point cloud. It has become a vital process in 3D computer graphics, vision and …

3D shape generation aims to produce innovative 3D content adhering to specific conditions
and constraints. Existing methods often decompose 3D shapes into a sequence of localized …

Point cloud completion aims to estimate the whole shapes of objects from their partial scans,
and one of the main obstacles that prevents current methods from being applied in real …

Point cloud completion aims to restore full shapes of objects from partial scans, and a typical
network pipeline is AutoEncoder, which has coarse-to-fine refinement modules. Although …

Large Reconstruction Models have made significant strides in the realm of automated 3D
content generation from single or multiple input images. Despite their success, these models …

Unpaired point cloud completion involves filling in missing parts of a point cloud without
requiring partial-complete correspondence. Meanwhile, since point cloud completion is an ill …

As 3D data acquisition techniques develop rapidly, different types of 3D scanners, like
LiDAR scanners and RGB-D cameras, are becoming popular in our daily life. More …

Our network adopts an encoder-decoder structure, which we implemented using PyTorch
[10]. To encode the input point cloud, we utilize the PCN architecture [19] which is a common …