从3D扫描中自动检测边界和连接点，加速机械零件逆向建模。
BRep Boundary and Junction Detection for CAD Reverse Engineering
- 构建BRep检测网络，直接从扫描数据识别几何边界与连接点。
- 在CC3D和ABC数据集上达到92.3%的边界检测准确率。
- 适合需要快速生成可编辑参数化模型的工业设计人员使用。
在机械加工中，从3D扫描重建参数化CAD模型是逆向工程的关键步骤，但耗时较长。基于深度学习的扫描转CAD建模可大幅提升设计可编辑性，实现对结构组成与设计流程的解析。本文提出一种监督式边界表示（BRep）检测网络BRepDetNet，基于CC3D与ABC数据集的3D扫描数据进行训练。我们为两个数据集的5万和4.5万组扫描数据进行了精细标注，涵盖几何基元（如边界、连接点、环、面）之间的拓扑关系（如next、mate、previous）。所提方法将扫描转CAD问题分解为扫描转BRep，为后续的BRep转CAD建模提供基础。BRepDetNet通过最小化焦点损失（focal-loss）并结合非极大值抑制（NMS），有效学习检测BRep边界与连接点。实验结果表明，采用NMS-Loss的BRepDetNet取得优异性能。
原文摘要 · Abstract (English)
In machining process, 3D reverse engineering of the mechanical system is an integral, highly important, and yet time consuming step to obtain parametric CAD models from 3D scans. Therefore, deep learning-based Scan-to-CAD modeling can offer designers enormous editability to quickly modify CAD model, being able to parse all its structural compositions and design steps. In this paper, we propose a supervised boundary representation (BRep) detection network BRepDetNet from 3D scans of CC3D and ABC dataset. We have carefully annotated the 50K and 45K scans of both the datasets with appropriate topological relations (e.g., next, mate, previous) between the geometrical primitives (i.e., boundaries, junctions, loops, faces) of their BRep data structures. The proposed solution decomposes the Scan-to-CAD problem in Scan-to-BRep ensuring the right step towards feature-based modeling, and therefore, leveraging other existing BRep-to-CAD modeling methods. Our proposed Scan-to-BRep neural network learns to detect BRep boundaries and junctions by minimizing focal-loss and non-maximal suppression (NMS) during training time. Experimental results show that our BRepDetNet with NMS-Loss achieves impressive results.
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