Point or Line? Using Line-based Representation for Panoptic Symbol Spotting in CAD Drawings

• URL: http://arxiv.org/abs/2505.23395v1
• Date: Thu, 29 May 2025 12:33:11 GMT
• Title: Point or Line? Using Line-based Representation for Panoptic Symbol Spotting in CAD Drawings
• Authors: Xingguang Wei, Haomin Wang, Shenglong Ye, Ruifeng Luo, Yanting Zhang, Lixin Gu, Jifeng Dai, Yu Qiao, Wenhai Wang, Hongjie Zhang,
• Abstract summary: We study the task of panoptic symbol spotting in computer-aided design (CAD) drawings composed of vector graphical primitives.<n>Existing methods typically rely on imageization, graph construction, or point-based representation.<n>We propose VecFormer, a novel method that addresses these challenges through line-based representation of primitives.
• Score: 45.116136045440584
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study the task of panoptic symbol spotting, which involves identifying both individual instances of countable things and the semantic regions of uncountable stuff in computer-aided design (CAD) drawings composed of vector graphical primitives. Existing methods typically rely on image rasterization, graph construction, or point-based representation, but these approaches often suffer from high computational costs, limited generality, and loss of geometric structural information. In this paper, we propose VecFormer, a novel method that addresses these challenges through line-based representation of primitives. This design preserves the geometric continuity of the original primitive, enabling more accurate shape representation while maintaining a computation-friendly structure, making it well-suited for vector graphic understanding tasks. To further enhance prediction reliability, we introduce a Branch Fusion Refinement module that effectively integrates instance and semantic predictions, resolving their inconsistencies for more coherent panoptic outputs. Extensive experiments demonstrate that our method establishes a new state-of-the-art, achieving 91.1 PQ, with Stuff-PQ improved by 9.6 and 21.2 points over the second-best results under settings with and without prior information, respectively, highlighting the strong potential of line-based representation as a foundation for vector graphic understanding.

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