Pixel-Wise Symbol Spotting via Progressive Points Location for Parsing CAD Images
This work addresses a challenging task for CAD revision and management in architecture and engineering industries, but it is incremental as it builds on existing heatmap-based methods with specific improvements.
The paper tackles the problem of pixel-wise symbol spotting in CAD images for parsing drawings, proposing a method using Progressive Gaussian Kernels and local offset for keypoint detection, and achieves good generalization ability on a released real-world dataset of telecommunication equipment rooms.
Parsing Computer-Aided Design (CAD) drawings is a fundamental step for CAD revision, semantic-based management, and the generation of 3D prototypes in both the architecture and engineering industries. Labeling symbols from a CAD drawing is a challenging yet notorious task from a practical point of view. In this work, we propose to label and spot symbols from CAD images that are converted from CAD drawings. The advantage of spotting symbols from CAD images lies in the low requirement of labelers and the low-cost annotation. However, pixel-wise spotting symbols from CAD images is challenging work. We propose a pixel-wise point location via Progressive Gaussian Kernels (PGK) to balance between training efficiency and location accuracy. Besides, we introduce a local offset to the heatmap-based point location method. Based on the keypoints detection, we propose a symbol grouping method to redraw the rectangle symbols in CAD images. We have released a dataset containing CAD images of equipment rooms from telecommunication industrial CAD drawings. Extensive experiments on this real-world dataset show that the proposed method has good generalization ability.