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 …

Corresponding author} In this paper, we explore a novel framework, EGIInet (Explicitly
Guided Information Interaction Network), a model for View-guided Point cloud Completion …

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 …

Although deep networks based 3D reconstruction methods can recover the 3D geometry
given few inputs, they may produce unfaithful reconstruction when predicting occluded parts …

Recent advances in point cloud completion make it possible to simultaneously recover
complete shapes and fine details from partial point clouds captured by professional 3D …

In real-world scenarios, scanned point clouds are often incomplete due to occlusion issues.
The task of self-supervised point cloud completion involves reconstructing missing regions …

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 …

3D shape completion is traditionally solved using supervised training or by distribution
learning on complete shape examples. Recently self-supervised learning approaches that …

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 …

Point cloud classification and segmentation are crucial tasks for point cloud processing and
have wide range of applications, such as autonomous driving and robot grasping. Some …