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 …

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 …

The rapid development of point cloud learning has driven point cloud completion into a new
era. However, the information flows of most existing completion methods are solely …

Point cloud completion aims to recover the completed 3D shape of an object from its partial
observation. A common strategy is to encode the observed points to a global feature vector …

Point cloud completion referring to completing 3D shapes from partial 3D point clouds is a
fundamental problem for 3D point cloud analysis tasks. Benefiting from the development of …

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 …

Image-based 3D reconstruction is a long-established, ill-posed problem defined within the
scope of computer vision and graphics. The purpose of image-based 3D reconstruction is to …

Semantic scene completion (SSC) aims to complete a partial 3D scene and predict its
semantics simultaneously. Most existing works adopt the voxel representations, thus …

We propose a novel convolutional operator for the task of point cloud completion. One
striking characteristic of our approach is that, conversely to related work it does not require …

Point cloud completion concentrates on completing geometric and topological shapes from
incomplete 3D shapes. Nevertheless, the unordered nature of point clouds will hamper the …