  Point cloud filtering is a fundamental 3D vision task, which aims to remove
noise while recovering the underlying clean surfaces. State-of-the-art methods
remove noise by moving noisy points along stochastic trajectories to the clean
surfaces. These methods often require regularization within the training
objective and/or during post-processing, to ensure fidelity. In this paper, we
introduce StraightPCF, a new deep learning based method for point cloud
filtering. It works by moving noisy points along straight paths, thus reducing
discretization errors while ensuring faster convergence to the clean surfaces.
We model noisy patches as intermediate states between high noise patch variants
and their clean counterparts, and design the VelocityModule to infer a constant
flow velocity from the former to the latter. This constant flow leads to
straight filtering trajectories. In addition, we introduce a DistanceModule
that scales the straight trajectory using an estimated distance scalar to
attain convergence near the clean surface. Our network is lightweight and only
has $\sim530K$ parameters, being 17% of IterativePFN (a most recent point cloud
filtering network). Extensive experiments on both synthetic and real-world data
show our method achieves state-of-the-art results. Our method also demonstrates
nice distributions of filtered points without the need for regularization. The
implementation code can be found at: https://github.com/ddsediri/StraightPCF.
