  Frequency-domain learning draws attention due to its superior tradeoff
between inference accuracy and input data size. Frequency-domain learning in 2D
computer vision tasks has shown that 2D convolutional neural networks (CNN)
have a stationary spectral bias towards low-frequency channels so that
high-frequency channels can be pruned with no or little accuracy degradation.
However, frequency-domain learning has not been studied in the context of 3D
CNNs with 3D volumetric data. In this paper, we study frequency-domain learning
for volumetric-based 3D data perception to reveal the spectral bias and the
accuracy-input-data-size tradeoff of 3D CNNs. Our study finds that 3D CNNs are
sensitive to a limited number of critical frequency channels, especially
low-frequency channels. Experiment results show that frequency-domain learning
can significantly reduce the size of volumetric-based 3D inputs (based on
spectral bias) while achieving comparable accuracy with conventional
spatial-domain learning approaches. Specifically, frequency-domain learning is
able to reduce the input data size by 98% in 3D shape classification while
limiting the average accuracy drop within 2%, and by 98% in the 3D point cloud
semantic segmentation with a 1.48% mean-class accuracy improvement while
limiting the mean-class IoU loss within 1.55%. Moreover, by learning from
higher-resolution 3D data (i.e., 2x of the original image in the spatial
domain), frequency-domain learning improves the mean-class accuracy and
mean-class IoU by 3.04% and 0.63%, respectively, while achieving an 87.5% input
data size reduction in 3D point cloud semantic segmentation.
