TL;DR
This paper introduces a novel deep network for 3D point cloud analysis based on 'orderly disorder' theory, which enhances robustness to noise and data distribution changes by focusing on stable patterns within complex structures.
Contribution
The proposed method employs a dynamic link approach and cloning decomposition to extract and emphasize stable features, reducing parameters and improving performance under noisy conditions.
Findings
Outperforms state-of-the-art networks on benchmark datasets.
Maintains less than 10% performance drop under noisy conditions.
Reduces model complexity and alleviates vanishing-gradient issues.
Abstract
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…
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