A new XAI method for point cloud classification uses semantic segmentation to define meaningful perturbation regions and a point-shifting mechanism to compute saliency maps.
Interpreting Representation Quality of DNNs for 3D Point Cloud Processing
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
In this paper, we evaluate the quality of knowledge representations encoded in deep neural networks (DNNs) for 3D point cloud processing. We propose a method to disentangle the overall model vulnerability into the sensitivity to the rotation, the translation, the scale, and local 3D structures. Besides, we also propose metrics to evaluate the spatial smoothness of encoding 3D structures, and the representation complexity of the DNN. Based on such analysis, experiments expose representation problems with classic DNNs, and explain the utility of the adversarial training.
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation
A new XAI method for point cloud classification uses semantic segmentation to define meaningful perturbation regions and a point-shifting mechanism to compute saliency maps.