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 clouds captured in real-world applications are often incomplete due to the limited
sensor resolution, single viewpoint, and occlusion. Therefore, recovering the complete point …

Chamfer distance (CD) is a standard metric to measure the shape dissimilarity between
point clouds in point cloud completion, as well as a loss function for (deep) learning …

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 …

In this paper, we propose a novel network, SVDFormer, to tackle two specific challenges in
point cloud completion: understanding faithful global shapes from incomplete point clouds …

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

Unsupervised completion of real scene objects is of vital importance but still remains
extremely challenging in preserving input shapes, predicting accurate results, and adapting …

Due to the unsatisfactory performance of supervised methods on unpaired real-world scans,
point cloud completion via cross-domain adaptation has recently drawn growing attention …

The representation quantifies the geometric shape and topology of a building is a necessary
procedure for many urban planning applications. A sharp line framework is a high-level …

A point cloud is a discrete set of data points sampled from a 3D geometric surface. Chamfer
distance (CD) is a popular metric and training loss to measure the distances between point …