  Time-of-Flight (ToF) cameras are subject to high levels of noise and
distortions due to Multi-Path-Interference (MPI). While recent research showed
that 2D neural networks are able to outperform previous traditional
State-of-the-Art (SOTA) methods on denoising ToF-Data, little research on
learning-based approaches has been done to make direct use of the 3D
information present in depth images. In this paper, we propose an iterative
denoising approach operating in 3D space, that is designed to learn on 2.5D
data by enabling 3D point convolutions to correct the points' positions along
the view direction. As labeled real world data is scarce for this task, we
further train our network with a self-training approach on unlabeled real world
data to account for real world statistics. We demonstrate that our method is
able to outperform SOTA methods on several datasets, including two real world
datasets and a new large-scale synthetic data set introduced in this paper.
