Most existing point cloud upsampling methods have roughly three steps: feature extraction,
feature expansion and 3D coordinate prediction. However, they usually suffer from two …

Point cloud classification is a fundamental task in 3D applications. However, it is challenging
to achieve effective feature learning due to the irregularity and unordered nature of point …

Transformers have been at the heart of the Natural Language Processing (NLP) and
Computer Vision (CV) revolutions. The significant success in NLP and CV inspired exploring …

Transformer with its underlying attention mechanism and the ability to capture long-range
dependencies makes it become a natural choice for unordered point cloud data. However …

In recent years, the popularity of depth sensors and three-dimensional (3D) scanners has
led to the rapid development of 3D point clouds. A transformer is a type of deep neural …

Semantic segmentation of 3D point cloud is a key task in numerous intelligent transportation
system applications, eg, self-driving vehicles, traffic monitoring. Due to the sparsity and …

Convolution on 3D point clouds is widely researched yet far from perfect in geometric deep
learning. The traditional wisdom of convolution characterises feature correspondences …

Point cloud upsampling aims to generate dense and uniformly distributed point sets from a
sparse point cloud, which plays a critical role in 3D computer vision. Previous methods …

Outstanding effectiveness of transformers in visual tasks has resulted in its fast growth and
adoption in three dimensions (3D) vision tasks. Vision transformers have shown numerous …

In this work we present RepKPU an efficient network for point cloud upsampling. We
propose to promote upsampling performance by exploiting better shape representation and …