Point cloud completion is able to estimate the complete point cloud starting from the missing
point cloud, which obtains higher quality point cloud data for widely used in remote sensing …

The goal of seeking advanced solutions to the descriptions of particle shape, packing and
tomographic measurement were key areas promoted by Professor Reg Davies. In this paper …

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 …

Semantic scene completion (SSC) aims to simultaneously perform scene completion (SC)
and predict semantic categories of a 3D scene from a single depth and/or RGB image. Most …

Point cloud completion aims at completing shapes from their partial. Most existing methods
utilized shape's priors information for point cloud completion, such as inputting the partial …

Point clouds are essential 3D data representations utilized across various disciplines, often
requiring point cloud completion methods to address inherent incompleteness. Existing …

Point clouds in the real world are often sparse and incomplete. Point cloud completion aims
to restore incomplete point clouds into meaningful shapes. In recent years, point cloud …

Recovering geometric regularities from scanned mesh models with various types of surface
features has always been a challenging task in reverse engineering. To address this …

Point cloud completion is a fundamental yet not well-solved problem in 3D vision. Current
approaches often rely on 3D coordinate information and/or additional data (eg, images and …

In recent years, zero-shot learning has attracted the focus of many researchers, due to its
flexibility and generality. Many approaches have been proposed to achieve the zero-shot …