  Statistical Shape Modeling (SSM) is a valuable tool for investigating and
quantifying anatomical variations within populations of anatomies. However,
traditional correspondence-based SSM generation methods have a prohibitive
inference process and require complete geometric proxies (e.g., high-resolution
binary volumes or surface meshes) as input shapes to construct the SSM.
Unordered 3D point cloud representations of shapes are more easily acquired
from various medical imaging practices (e.g., thresholded images and surface
scanning). Point cloud deep networks have recently achieved remarkable success
in learning permutation-invariant features for different point cloud tasks
(e.g., completion, semantic segmentation, classification). However, their
application to learning SSM from point clouds is to-date unexplored. In this
work, we demonstrate that existing point cloud encoder-decoder-based completion
networks can provide an untapped potential for SSM, capturing population-level
statistical representations of shapes while reducing the inference burden and
relaxing the input requirement. We discuss the limitations of these techniques
to the SSM application and suggest future improvements. Our work paves the way
for further exploration of point cloud deep learning for SSM, a promising
avenue for advancing shape analysis literature and broadening SSM to diverse
use cases.
