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

TL;DR

This paper presents MiCADangelo, a novel method for converting 3D scans into detailed, editable CAD models that incorporate sketch constraints, addressing limitations of existing approaches by capturing fine geometric details and structural constraints.

Contribution

MiCADangelo introduces a new approach using multi-plane cross-sections to improve fine-grained reconstruction and explicitly incorporate sketch constraints into CAD reverse engineering.

Findings

Outperforms state-of-the-art methods in reconstruction quality

Successfully incorporates sketch constraints into CAD models

Produces detailed and editable CAD reconstructions from 3D scans

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.