English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This work studies the problem of panoptic symbol spotting, which is to spotand parse both countable object instances (windows, doors, tables, etc.) anduncountable stuff (wall, railing, etc.) from computer-aided design (CAD)drawings. Existing methods typically involve either rasterizing the vectorgraphics into images and using image-based methods for symbol spotting, ordirectly building graphs and using graph neural networks for symbolrecognition. In this paper, we take a different approach, which treats graphicprimitives as a set of 2D points that are locally connected and use point cloudsegmentation methods to tackle it. Specifically, we utilize a point transformerto extract the primitive features and append a mask2former-like spotting headto predict the final output. To better use the local connection information ofprimitives and enhance their discriminability, we further propose the attentionwith connection module (ACM) and contrastive connection learning scheme (CCL).Finally, we propose a KNN interpolation mechanism for the mask attention moduleof the spotting head to better handle primitive mask downsampling, which isprimitive-level in contrast to pixel-level for the image. Our approach, namedSymPoint, is simple yet effective, outperforming recent state-of-the-art methodGAT-CADNet by an absolute increase of 9.6% PQ and 10.4% RQ on the FloorPlanCADdataset. The source code and models will be available athttps://github.com/nicehuster/SymPoint.

Symbol as Points: Panoptic Symbol Spotting via Point-based Representation