ArchCAD-400K: A Large-Scale CAD drawings Dataset and New Baseline for Panoptic Symbol Spotting
Year: 2025
Type: preprint
Abstract: Recognizing symbols in architectural CAD drawings is critical for various advanced engineering applications. In this paper, we propose a novel CAD data annotation engine that leverages intrinsic attributes from systematically archived CAD drawings to automatically generate high-quality annotations, thus significantly reducing manual labeling efforts. Utilizing this engine, we construct ArchCAD-400K, a large-scale CAD dataset consisting of 413,062 chunks from 5538 highly standardized drawings, making it over 26 times larger than the largest existing CAD dataset. ArchCAD-400K boasts an extended drawing diversity and broader categories, offering line-grained annotations. Furthermore, we present a new baseline model for panoptic symbol spotting, termed Dual-Pathway Symbol Spotter (DPSS). It incorporates an adaptive fusion module to enhance primitive features with complementary image features, achieving state-of-the-art performance and enhanced robustness. Extensive experiments validate the effectiveness of DPSS, demonstrating the value of ArchCAD-400K and its potential to drive innovation in architectural design and construction.
Source: arXiv (Cornell University)
Authors: Luo, Ruifeng, Liu, Zhengjie, Cheng, Tianxiao, Wang, Jie, Wang, Tongjie, Wei, Xingguang, Wang, Haomin, Li, YanPeng, Chai, Fu, Cheng, Fei, Ye, Shenglong, Wang, Wenhai, Zhang, Yanting, Qiao, Yu, Zhang, Hongjie, Zhao, Xianzhong
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6979276127
Cites:
Cited by:
Related to: