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 data have been widely explored due to its superior accuracy and robustness
under various adverse situations. Meanwhile, deep neural networks (DNNs) have achieved …

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 …

Point cloud completion aims to predict a complete shape in high accuracy from its partial
observation. However, previous methods usually suffered from discrete nature of point cloud …

Real-scanned point clouds are often incomplete due to viewpoint, occlusion, and noise.
Existing point cloud completion methods tend to generate global shape skeletons and …

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 …

Point cloud completion concerns to predict missing part for incomplete 3D shapes. A
common strategy is to generate complete shape according to incomplete input. However …

The task of point cloud completion aims to predict the missing part for an incomplete 3D
shape. A widely used strategy is to generate a complete point cloud from the incomplete …

Most 3D shape completion approaches rely heavily on partial-complete shape pairs and
learn in a fully supervised manner. Despite their impressive performances on in-domain …

Chamfer Distance (CD) and Earth Mover's Distance (EMD) are two broadly adopted metrics
for measuring the similarity between two point sets. However, CD is usually insensitive to …