  Point cloud obtained from 3D scanning is often sparse, noisy, and irregular.
To cope with these issues, recent studies have been separately conducted to
densify, denoise, and complete inaccurate point cloud. In this paper, we
advocate that jointly solving these tasks leads to significant improvement for
point cloud reconstruction. To this end, we propose a deep point cloud
reconstruction network consisting of two stages: 1) a 3D sparse
stacked-hourglass network as for the initial densification and denoising, 2) a
refinement via transformers converting the discrete voxels into 3D points. In
particular, we further improve the performance of transformer by a newly
proposed module called amplified positional encoding. This module has been
designed to differently amplify the magnitude of positional encoding vectors
based on the points' distances for adaptive refinements. Extensive experiments
demonstrate that our network achieves state-of-the-art performance among the
recent studies in the ScanNet, ICL-NUIM, and ShapeNetPart datasets. Moreover,
we underline the ability of our network to generalize toward real-world and
unmet scenes.
