  Point clouds captured by depth sensors are often contaminated by noises,
obstructing further analysis and applications. In this paper, we emphasize the
importance of point distribution uniformity to downstream tasks. We demonstrate
that point clouds produced by existing gradient-based denoisers lack uniformity
despite having achieved promising quantitative results. To this end, we propose
GPCD++, a gradient-based denoiser with an ultra-lightweight network named
UniNet to address uniformity. Compared with previous state-of-the-art methods,
our approach not only generates competitive or even better denoising results,
but also significantly improves uniformity which largely benefits applications
such as surface reconstruction.
