CADMorph: Geometry-Driven Parametric CAD Editing via a Plan-Generate-Verify Loop

2025-12-12

AI Summary

This work addresses the geometrically driven parametric CAD editing challenge: simultaneously and structurally preserving updates to a CAD model’s underlying parametric construction sequence during shape editing—while ensuring structural consistency, semantic validity, and high shape fidelity—particularly under scarce triplet supervision. We propose a “Plan-Generate-Verify” closed-loop framework: (1) zero-shot editing via a parameter-to-shape (P2S) latent diffusion model coupled with masked parameter prediction (MPP); (2) cross-attention mechanisms to localize edit regions and inject semantic context; and (3) automatic verification using shape latent-space distance metrics. Our method requires no manually annotated triplets and significantly outperforms GPT-4o and specialized CAD baselines. It achieves comprehensive success across structural preservation, semantic plausibility, and shape fidelity, enabling high-fidelity iterative editing and enhanced reverse engineering capabilities.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

A Computer-Aided Design (CAD) model encodes an object in two coupled forms: a parametric construction sequence and its resulting visible geometric shape. During iterative design, adjustments to the geometric shape inevitably require synchronized edits to the underlying parametric sequence, called geometry-driven parametric CAD editing. The task calls for 1) preserving the original sequence's structure, 2) ensuring each edit's semantic validity, and 3) maintaining high shape fidelity to the target shape, all under scarce editing data triplets. We present CADMorph, an iterative plan-generate-verify framework that orchestrates pretrained domain-specific foundation models during inference: a parameter-to-shape (P2S) latent diffusion model and a masked-parameter-prediction (MPP) model. In the planning stage, cross-attention maps from the P2S model pinpoint the segments that need modification and offer editing masks. The MPP model then infills these masks with semantically valid edits in the generation stage. During verification, the P2S model embeds each candidate sequence in shape-latent space, measures its distance to the target shape, and selects the closest one. The three stages leverage the inherent geometric consciousness and design knowledge in pretrained priors, and thus tackle structure preservation, semantic validity, and shape fidelity respectively. Besides, both P2S and MPP models are trained without triplet data, bypassing the data-scarcity bottleneck. CADMorph surpasses GPT-4o and specialized CAD baselines, and supports downstream applications such as iterative editing and reverse-engineering enhancement.

Problem

• Geometry-driven parametric CAD editing requires synchronized shape and parameter sequence adjustments
• Preserving original structure, ensuring semantic validity, and maintaining shape fidelity under scarce data
• Automating iterative CAD design while bypassing data-scarcity bottlenecks in editing

Innovation

• Plan-generate-verify loop with pretrained foundation models
• Cross-attention maps pinpoint modifications for structure preservation
• Shape-latent space verification ensures high fidelity to target