  The quality of point clouds is often limited by noise introduced during their
capture process. Consequently, a fundamental 3D vision task is the removal of
noise, known as point cloud filtering or denoising. State-of-the-art learning
based methods focus on training neural networks to infer filtered displacements
and directly shift noisy points onto the underlying clean surfaces. In high
noise conditions, they iterate the filtering process. However, this iterative
filtering is only done at test time and is less effective at ensuring points
converge quickly onto the clean surfaces. We propose IterativePFN (iterative
point cloud filtering network), which consists of multiple IterationModules
that model the true iterative filtering process internally, within a single
network. We train our IterativePFN network using a novel loss function that
utilizes an adaptive ground truth target at each iteration to capture the
relationship between intermediate filtering results during training. This
ensures that the filtered results converge faster to the clean surfaces. Our
method is able to obtain better performance compared to state-of-the-art
methods. The source code can be found at:
https://github.com/ddsediri/IterativePFN.
