VoxelDiffusionCut: Non-destructive Internal-part Extraction via Iterative Cutting and Structure Estimation

2026-03-03 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

VoxelDiffusionCut is a novel framework designed for the non-destructive extraction of target internal parts, such as batteries or motors, from products with unknown internal structures, a critical task in recycling and disposal. The method iteratively estimates the product's internal structure from observed cutting surfaces and then formulates cutting plans. It addresses challenges in conditional generative modeling, specifically the high dimensionality of 3D shape representations and mode collapse in conventional models, by using a diffusion model to complete voxel representations of internal structures. This approach captures multi-modal predictive uncertainty in unobserved regions, mitigating overconfident predictions and reducing erroneous cuts. Experimental results in a simulator, using both simple and complex-shaped models, demonstrate that VoxelDiffusionCut effectively estimates internal structures and enables non-destructive extraction by leveraging estimated uncertainty, outperforming baseline methods like CVAE-based models and direct point cloud diffusion models.

Key takeaway

For AI Scientists developing autonomous dismantling systems, VoxelDiffusionCut offers a robust approach to non-destructive internal part extraction. You should consider implementing voxel-based diffusion models for internal structure estimation, especially when dealing with unknown product configurations and the need for uncertainty-aware cutting plans. This method's ability to capture multi-modal uncertainty can significantly reduce the risk of damaging valuable components, improving safety and recovery rates in recycling operations.

Key insights

Iterative internal structure estimation via diffusion models enables non-destructive part extraction from unknown products.

Principles

• Voxel representation simplifies 3D shape learning.
• Diffusion models capture multi-modal predictive uncertainty.
• Uncertainty-aware planning reduces erroneous cuts.

Method

VoxelDiffusionCut iteratively estimates internal structure using a conditional diffusion model on voxelized data, then plans cuts to maximize removable volume while avoiding target part damage based on estimated uncertainty.

In practice

• Use voxelization for tractable 3D shape learning.
• Employ diffusion models for uncertainty-aware predictions.
• Adjust cutting-risk threshold (η) for desired conservativeness.

Topics

• Diffusion Models
• Autonomous Dismantling
• Voxel Representation
• Internal Part Extraction
• 3D Shape Completion

Best for: AI Scientist, Research Scientist, AI Engineer, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.