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 …

While previous studies have demonstrated successful 3D object shape completion with a
sufficient number of points they often fail in scenarios when a few points eg tens of points are …

Point cloud completion aims to recover accurate global geometry and preserve fine-grained
local details from partial point clouds. Conventional methods typically predict unseen points …

Shape retrieval and alignment are a promising avenue towards turning 3D scans into
lightweight CAD representations that can be used for content creation such as mobile or …

Many point cloud completion methods typically rely on two steps: coarse generation and 2D
Grid deformed fine output. However, in the fine generation, the expansion range (2D Grid …

In recent years, with the rapid advancement of intelligent generative algorithms and the
continuous improvement of computing power, the application of artificial intelligence …

Point cloud completion aims to utilize algorithms to repair missing parts in 3D data for high-
quality point clouds. This technology is crucial for applications such as autonomous driving …

In the Cultural Heritage (CH) domain, the semantic segmentation of 3D point clouds with
Deep Learning (DL) techniques allows to recognize historical architectural elements, at a …