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 has become increasingly popular among generation tasks of 3D
point clouds, as it is a challenging yet indispensable problem to recover the complete shape …

Problems such as equipment defects or limited viewpoints will lead the captured point
clouds to be incomplete. Therefore, recovering the complete point clouds from the partial …

Point cloud completion aims at completing geometric and topological shapes from a partial
observation. However, some topology of the original shape is missing, existing methods …

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 …

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 …

Place recognition based on point clouds (LiDAR) is an important component for autonomous
robots or self-driving vehicles. Current SOTA performance is achieved on accumulated …

Scene reconstructions are often incomplete due to occlusions and limited viewpoints. There
have been efforts to use semantic information for scene completion. However, the completed …

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 …