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.
@article{arxiv.2505.23395,
title = {Point or Line? Using Line-based Representation for Panoptic Symbol Spotting in CAD Drawings},
author = {Xingguang Wei and Haomin Wang and Shenglong Ye and Ruifeng Luo and Yanting Zhang and Lixin Gu and Jifeng Dai and Yu Qiao and Wenhai Wang and Hongjie Zhang},
journal= {arXiv preprint arXiv:2505.23395},
year = {2025}
}