  Point clouds are essential for storage and transmission of 3D content. As
they can entail significant volumes of data, point cloud compression is crucial
for practical usage. Recently, point cloud geometry compression approaches
based on deep neural networks have been explored. In this paper, we evaluate
the ability to predict perceptual quality of typical voxel-based loss functions
employed to train these networks. We find that the commonly used focal loss and
weighted binary cross entropy are poorly correlated with human perception. We
thus propose a perceptual loss function for 3D point clouds which outperforms
existing loss functions on the ICIP2020 subjective dataset. In addition, we
propose a novel truncated distance field voxel grid representation and find
that it leads to sparser latent spaces and loss functions that are more
correlated with perceived visual quality compared to a binary representation.
The source code is available at
https://github.com/mauriceqch/2021_pc_perceptual_loss.
