Introduces 3D-CBM framework mapping raw 3D inputs to multi-tiered interpretable concepts, achieving 88.8% concept accuracy and test-time intervention on PartNet and ShapeNet.
Explainability of point cloud neural networks using smile: Statistical model-agnostic interpretability with local explanations
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3D-CBM: A Framework for Concept-Based Interpretability in Generative 3D Modeling
Introduces 3D-CBM framework mapping raw 3D inputs to multi-tiered interpretable concepts, achieving 88.8% concept accuracy and test-time intervention on PartNet and ShapeNet.