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In the real world, out-of-distribution samples, noise and distortions existin test data. Existing deep networks developed for point cloud data analysisare prone to overfitting and a partial change in test data leads tounpredictable behaviour of the networks. In this paper, we propose a smart yetsimple deep network for analysis of 3D models using `orderly disorder' theory.Orderly disorder is a way of describing the complex structure of disorderswithin complex systems. Our method extracts the deep patterns inside a 3Dobject via creating a dynamic link to seek the most stable patterns and atonce, throws away the unstable ones. Patterns are more robust to changes indata distribution, especially those that appear in the top layers. Features areextracted via an innovative cloning decomposition technique and then linked toeach other to form stable complex patterns. Our model alleviates thevanishing-gradient problem, strengthens dynamic link propagation andsubstantially reduces the number of parameters. Extensive experiments onchallenging benchmark datasets verify the superiority of our light network onthe segmentation and classification tasks, especially in the presence of noisewherein our network's performance drops less than 10% while thestate-of-the-art networks fail to work.

Orderly Disorder in Point Cloud Domain