PU-Transformer: Point Cloud Upsampling Transformer

Understand

Given the rapid development of 3D scanners, point clouds are becoming popular in AI-driven machines.

• However, point cloud data is inherently sparse and irregular, causing significant difficulties for machine perception.
• In this work, we focus on the point cloud upsampling task that intends to generate dense high-fidelity point clouds from sparse input data.
• Specifically, to activate the transformer's strong capability in representing features, we develop a new variant of a multi-head self-attention structure to enhance both point-wise and channel-wise relations of the feature map.