  This work presents an innovative method for point set self-embedding, that
encodes the structural information of a dense point set into its sparser
version in a visual but imperceptible form. The self-embedded point set can
function as the ordinary downsampled one and be visualized efficiently on
mobile devices. Particularly, we can leverage the self-embedded information to
fully restore the original point set for detailed analysis on remote servers.
This task is challenging since both the self-embedded point set and the
restored point set should resemble the original one. To achieve a learnable
self-embedding scheme, we design a novel framework with two jointly-trained
networks: one to encode the input point set into its self-embedded sparse point
set and the other to leverage the embedded information for inverting the
original point set back. Further, we develop a pair of up-shuffle and
down-shuffle units in the two networks, and formulate loss terms to encourage
the shape similarity and point distribution in the results. Extensive
qualitative and quantitative results demonstrate the effectiveness of our
method on both synthetic and real-scanned datasets.
