  Raw point cloud processing using capsule networks is widely adopted in
classification, reconstruction, and segmentation due to its ability to preserve
spatial agreement of the input data. However, most of the existing capsule
based network approaches are computationally heavy and fail at representing the
entire point cloud as a single capsule. We address these limitations in
existing capsule network based approaches by proposing PointCaps, a novel
convolutional capsule architecture with parameter sharing. Along with
PointCaps, we propose a novel Euclidean distance routing algorithm and a
class-independent latent representation. The latent representation captures
physically interpretable geometric parameters of the point cloud, with dynamic
Euclidean routing, PointCaps well-represents the spatial (point-to-part)
relationships of points. PointCaps has a significantly lower number of
parameters and requires a significantly lower number of FLOPs while achieving
better reconstruction with comparable classification and segmentation accuracy
for raw point clouds compared to state-of-the-art capsule networks.
