Point cloud completion is a generation and estimation issue derived from the partial point
clouds, which plays a vital role in the applications of 3D computer vision. The progress of …

Point cloud completion aims to predict a complete shape in high accuracy from its partial
observation. However, previous methods usually suffered from discrete nature of point cloud …

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 …

The task of point cloud completion aims to predict the missing part for an incomplete 3D
shape. A widely used strategy is to generate a complete point cloud from the incomplete …

Most existing point cloud completion methods suffer from the discrete nature of point clouds
and the unstructured prediction of points in local regions, which makes it difficult to reveal …

We propose a novel approach aimed at object and semantic scene completion from a partial
scan represented as a 3D point cloud. Our architecture relies on three novel layers that are …

With the growing availability of extensive 3D datasets and the rapid progress in
computational power, deep learning (DL) has emerged as a highly promising approach for …

Deep learning technique has yielded significant improvements in point cloud completion
with the aim of completing missing object shapes from partial inputs. However, most existing …

We tackle the problem of object completion from point clouds and propose a novel point
cloud completion network employing an Asymmetrical Siamese Feature Matching strategy …

Point cloud shape completion plays a central role in diverse 3D vision and robotics
applications. Early methods used to generate global shapes without local detail refinement …