arXiv：cs.AI· Xintong Dong, Chuanyang Li, Peng Zheng, Chuqi Han, Jiaxin Jing, Hailong Shen, Yanzhi Song, Zhouwang Yang·· 3 小时前
HistCAD：面向可编辑性评估的约束感知参数化 CAD 历史数据集
HistCAD: Constraint-Aware Parametric CAD Histories for Evaluating Editability
AI 导读
HistCAD 是一个可执行表示与数据集，其 Academic 与 Industrial 两个集合共含 180,495 条带实体引用草图约束和保留特征操作的参数化构建历史，用于评估预测约束能否复现初始模型并支持指定尺寸编辑。
正文
Abstract:Sketch constraints specify geometric conditions for constructing and modifying parametric CAD models. We study whether predicted constraints allow a given history to reproduce the required initial model and support prescribed dimensional edits. We introduce HistCAD, an executable representation and dataset whose Academic and Industrial collections contain 180,495 parametric construction histories with entity-referenced sketch constraints and retained feature operations. Predictors receive these histories with the geometry and feature definitions retained and explicit sketch constraints removed. They generate constraints for every sketch without seeing the edit request. The benchmark compares models built with alternative constraint sets for the same history under the same dimensional edit. An edit succeeds when the model reproduces the required initial geometry, reaches the target value, preserves specified relations and unedited dimensions in the target sketch, and rebuilds through the complete history. A predictor trained on both collections and supplied with descriptions of the input histories achieves overall edit success of 52.4% on Academic and 29.0% on Industrial. Models retaining only endpoint-connectivity constraints in the target sketch and the history's constraints elsewhere can reach the target and rebuild while failing preservation. For all-sketch predictions, we retain the target-sketch prediction and restore the history's constraints in other sketches. More models then reproduce the required initial geometry, and some of these newly matched models complete the edit. HistCAD connects constraint learning to the construction and revision of parametric CAD models.
| Subjects: | Graphics (cs.GR); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2602.19171 [cs.GR] |
| (or arXiv:2602.19171v4 [cs.GR] for this version) | |
| https://doi.org/10.48550/arXiv.2602.19171 arXiv-issued DOI via DataCite |
Submission history
From: Xintong Dong [view email]
[v1]
Mon, 8 Dec 2025 05:52:14 UTC (8,590 KB)
[v2]
Sun, 3 May 2026 13:28:32 UTC (4,301 KB)
[v3]
Fri, 29 May 2026 07:18:12 UTC (4,301 KB)
[v4]
Thu, 8 Oct 2026 04:23:53 UTC (4,513 KB)
来源：arXiv：cs.AI · arxiv.org