3D-CBM: A Framework for Concept-Based Interpretability in Generative 3D Modeling

2026-06-09

AI Summary

This work addresses the lack of interpretability in 3D generative models for deep geometric learning by introducing Concept Bottleneck Models (CBMs) into unstructured 3D data generation, establishing an intrinsically interpretable framework. The proposed method maps point clouds or meshes onto a hierarchy of human-understandable semantic concepts—such as part-level structures and functional attributes—enabling interactive intervention and semantic manipulation at test time. Experiments on PartNet and ShapeNet demonstrate that the model achieves a part-level concept prediction accuracy of 88.8% and a Chamfer Distance of 0.0115, while effectively correcting structural errors. This approach provides a foundational architecture for human-in-the-loop collaborative design in 3D shape generation.

Abstract

This research introduces a framework for incorporating Concept Bottleneck Models (CBMs) into 3D generative architectures to address the inherent 'semantic gap' in deep geometric learning. As deep models become central to 3D content creation, explainability shifts from a peripheral feature to a fundamental requirement for trust and accountability in safety-critical domains such as healthcare and manufacturing. CBMs provide an intrinsic interpretability solution by constraining latent representations to align with human-defined concepts, yet their application to unstructured 3D data remains largely unexplored. We design, implement, and validate a formal 3D-CBM architecture that maps raw geometric inputs, including point clouds and meshes, into a multi-tiered taxonomy of interpretable primitives and functional attributes. The framework further identifies strategic datasets, such as PartNet and ShapeNet, specialized for concept-based supervision. Experimental results from a 3D part-manipulation proof-of-concept experiment demonstrate the framework's efficacy, achieving a concept prediction accuracy of 88.8\% and a Chamfer Distance of 0.0115. Critically, the model enables precise test-time intervention, allowing for the interactive correction of structural errors. This work establishes a foundation for semantically-steerable 3D generation and invites further exploration into collaborative human-in-the-loop design systems.

Problem

• semantic gap
• 3D generative modeling
• concept-based interpretability
• explainable AI
• human-defined concepts

Innovation

• Concept Bottleneck Models
• 3D Generative Modeling
• Interpretable AI
• Semantic Control
• Human-in-the-Loop