  In the real world, out-of-distribution samples, noise and distortions exist
in test data. Existing deep networks developed for point cloud data analysis
are prone to overfitting and a partial change in test data leads to
unpredictable behaviour of the networks. In this paper, we propose a smart yet
simple deep network for analysis of 3D models using `orderly disorder' theory.
Orderly disorder is a way of describing the complex structure of disorders
within complex systems. Our method extracts the deep patterns inside a 3D
object via creating a dynamic link to seek the most stable patterns and at
once, throws away the unstable ones. Patterns are more robust to changes in
data distribution, especially those that appear in the top layers. Features are
extracted via an innovative cloning decomposition technique and then linked to
each other to form stable complex patterns. Our model alleviates the
vanishing-gradient problem, strengthens dynamic link propagation and
substantially reduces the number of parameters. Extensive experiments on
challenging benchmark datasets verify the superiority of our light network on
the segmentation and classification tasks, especially in the presence of noise
wherein our network's performance drops less than 10% while the
state-of-the-art networks fail to work.
