  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. In addition, we
leverage a positional fusion block to comprehensively capture the local context
of point cloud data, providing more position-related information about the
scattered points. As the first transformer model introduced for point cloud
upsampling, we demonstrate the outstanding performance of our approach by
comparing with the state-of-the-art CNN-based methods on different benchmarks
quantitatively and qualitatively.
