PU-SSIM: A Perceptual Constraint for Point Cloud Up-Sampling

Point cloud data acquired through scanning typically exhibits sparse, non-uniform distribution, and a certain level of noise. Therefore, it is necessary to generate a dense and high-quality point cloud via up-sampling. In recent years, point cloud up-sampling techniques gain significant advantages due to the development of deep learning. In particular, most of current end-to-end up-sampling networks adopt point-wise constraints, e.g., Chamfer distance to train the up-sampling model. However, these point-wise constraints are inadequate to reduce residual noise, meanwhile would induce structural distortions. To further improve the capability of up-sampling networks, we propose a perception-wise constraints, namely PU-SSIM. Specifically, we adopt the typical full-reference point cloud quality metric to measure the structural similarity between the generated high-resolution point cloud and the ground truth. We managed to embed it into the up-sampling network, providing a plug-in capability. The experimental results indicate that the PU-SSIM can maintain the structural details, meanwhile reduce the residual noises. The proposed perception constraint is compatible to most mainstream methods, which would benefit the community to some extend.