将3D扫描转为可编辑的精细参数化CAD模型，支持草图约束。
MiCADangelo: Fine-Grained Reconstruction of Constrained CAD Models from 3D Scans
- 利用多平面截面提取2D图案，捕捉参数化细节。
- 首次在重建中融入草图级约束，提升精度与可编辑性。
- 优于现有方法，适合工业设计与逆向工程场景。
计算机辅助设计（CAD）在现代制造和产品开发中起着基础作用，常需对现有模型进行修改或扩展。将3D扫描转化为参数化CAD表示——即CAD逆向工程——仍面临重大挑战，原因在于CAD模型对精度和结构复杂性的高要求。现有基于深度学习的方法主要分为两类：自下而上的几何驱动方法通常无法生成完全参数化的输出；自上而下的策略则往往忽略细粒度几何细节。此外，当前方法忽视了CAD建模的一个关键方面：草图级约束。本文提出一种受人类设计师操作启发的新方法。该方法利用多平面截面提取2D模式，更有效地捕捉参数化细节，实现详细且可编辑的CAD模型重建，优于现有最先进方法，并首次将草图约束直接整合到重建流程中。
原文摘要 · Abstract (English)
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.
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