SubDGuide: A Modeler-Inspired Agentic Workflow for Mesh-to-SubD Reconstruction

2026-10-08

AI Summary

This study addresses the challenges of recovering sparse control cages from dense meshes, including inferring control requirements, missing feature curves, and difficulty in correcting initial results. To overcome these issues, this work proposes a modeler-inspired agent workflow that iteratively optimizes through planning and diagnosis phases. A stateful feedback mechanism is introduced to support repair or rollback decisions by incorporating semantic judgments into the pipeline. To ensure inspectability, the planner avoids directly generating vertices, instead integrating multi-view geometric evidence analysis with tool execution verification. Evaluated on a fixed benchmark queue, the proposed method outperforms automatic remeshing approaches across five metrics, reducing Chamfer-L1 to 0.443% and improving the F-score to 92.78%, thereby achieving highly controllable mesh-to-subdivision surface reconstruction.

Abstract

Subdivision surfaces represent free-form geometry through a sparse control cage, but recovering that cage from a dense mesh is not merely a fitting problem: the system must infer where control is needed, which curves encode design features, and when an initial result should be revised. We present SubDGuide, a modeler-inspired agentic workflow for this task. Stage A reads aligned multiview geometric evidence and produces a compact plan for cage resolution, feature mapping, and broad-form fitting. Stage B inspects the resulting surface, requests targeted diagnostics, and chooses repair, rollback, or stopping actions. Geometry tools execute and verify every change; the planner never generates vertices or connectivity. On a fixed evaluation cohort, SubDGuide improves all five reported metrics over automatic remeshing: median Chamfer-L1 decreases from 0.641% to 0.443% of the target bounding-box diagonal, and F-score at a 1% tolerance increases from 82.00% to 92.78%. Stateful feedback improves four of five reported median metrics over one-shot planning, while verification retains the earlier checkpoint when a proposal is unhelpful. The same interface supports six multimodal planners, showing how semantic judgment can guide a practical, inspectable mesh-to-SubD workflow.

Problem

• Mesh-to-SubD reconstruction
• Subdivision surfaces
• Control cage recovery
• Reverse engineering

Innovation

• Agentic Workflow
• Subdivision Surfaces
• Mesh-to-SubD Reconstruction
• Stateful Feedback
• Multimodal Planners