The success of the transformer architecture in natural language processing has recently
triggered attention in the computer vision field. The transformer has been used as a …

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 …

Indoor point clouds from real-world scans are often incomplete and sparse due to limited
observation views and severe occlusion between objects. Point cloud completion can …

Unsupervised point cloud completion aims to infer the whole geometry of a partial object
observation without requiring partial-complete correspondence. Differing from existing …

Abstract 3D shape completion is traditionally solved using supervised training or by
distribution learning on complete shape examples. Recently self-supervised learning …

In the practical application of point cloud completion tasks, real data quality is usually much
worse than the CAD datasets used for training. A small amount of noisy data will usually …

The automatic pulmonary segmentation for chest X-ray (CXR) plays an important role in
assisting diagnosis. Many deep learning methods have the problems of high computational …

In this work, we propose a novel point cloud completion network, called CompleteDT. To
fully capture the 3D geometric structure of point clouds, we introduce an Information …

Point Cloud Completion aims to reconstruct the complete 3D shapes based on partial 3D
point clouds. Existing methods typically require either complete point clouds or multiple …