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.
@article{arxiv.2510.23429,
title = {MiCADangelo: Fine-Grained Reconstruction of Constrained CAD Models from 3D Scans},
author = {Ahmet Serdar Karadeniz and Dimitrios Mallis and Danila Rukhovich and Kseniya Cherenkova and Anis Kacem and Djamila Aouada},
journal= {arXiv preprint arXiv:2510.23429},
year = {2025}
}