MiCADangelo: Fine-Grained Reconstruction of Constrained CAD Models from 3D Scans

2025-10-27

AI Summary

In CAD reverse engineering, high-fidelity reconstruction of editable, parametric CAD models from 3D scan data—preserving both geometric fidelity and structural semantics—remains challenging. This paper proposes the first method to explicitly incorporate sketch-level geometric constraints into the reverse reconstruction pipeline. Leveraging deep learning–driven multi-planar cross-sectional analysis, it automatically detects 2D geometric primitives from scan data and maps them onto a 3D parametric modeling framework. A joint optimization strategy unifies constraint solving and parameter fitting, enabling fine-grained geometric recovery while preserving topologically consistent, semantically meaningful constraints. Experiments demonstrate that our approach significantly outperforms state-of-the-art methods in geometric accuracy, topological integrity, and model editability. It establishes a novel paradigm for automated, constraint-aware CAD reconstruction from raw 3D scans.

Technology Category

• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition
• Computer Vision: 3D Computer Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Computer-Aided Design (CAD) plays a foundational role in modern manufacturing and product development, often requiring designers to modify or build upon existing models. Converting 3D scans into parametric CAD representations--a process known as CAD reverse engineering--remains a significant challenge due to the high precision and structural complexity of CAD models. Existing deep learning-based approaches typically fall into two categories: bottom-up, geometry-driven methods, which often fail to produce fully parametric outputs, and top-down strategies, which tend to overlook fine-grained geometric details. Moreover, current methods neglect an essential aspect of CAD modeling: sketch-level constraints. In this work, we introduce a novel approach to CAD reverse engineering inspired by how human designers manually perform the task. Our method leverages multi-plane cross-sections to extract 2D patterns and capture fine parametric details more effectively. It enables the reconstruction of detailed and editable CAD models, outperforming state-of-the-art methods and, for the first time, incorporating sketch constraints directly into the reconstruction process.

Problem

• Converting 3D scans into parametric CAD models
• Capturing fine-grained geometric details in reconstruction
• Incorporating sketch-level constraints into CAD reverse engineering

Innovation

• Uses multi-plane cross-sections for 2D pattern extraction
• Reconstructs editable CAD models with parametric details
• Incorporates sketch constraints directly into reconstruction process