Verifiability and Predictability: Interpreting Utilities of Network Architectures for Point Cloud Processing
This work addresses the interpretability and optimization of network architectures for point cloud processing, offering incremental improvements for researchers and practitioners in 3D vision.
The paper diagnoses deep neural networks for 3D point cloud processing to explore how intermediate-layer architectures affect representation capacity, proposing and verifying hypotheses using five metrics, and uses these insights to revise architectures and improve utilities.
In this paper, we diagnose deep neural networks for 3D point cloud processing to explore utilities of different intermediate-layer network architectures. We propose a number of hypotheses on the effects of specific intermediate-layer network architectures on the representation capacity of DNNs. In order to prove the hypotheses, we design five metrics to diagnose various types of DNNs from the following perspectives, information discarding, information concentration, rotation robustness, adversarial robustness, and neighborhood inconsistency. We conduct comparative studies based on such metrics to verify the hypotheses. We further use the verified hypotheses to revise intermediate-layer architectures of existing DNNs and improve their utilities. Experiments demonstrate the effectiveness of our method.