  SymPoint is an initial attempt that utilizes point set representation to
solve the panoptic symbol spotting task on CAD drawing. Despite its
considerable success, it overlooks graphical layer information and suffers from
prohibitively slow training convergence. To tackle this issue, we introduce
SymPoint-V2, a robust and efficient solution featuring novel, streamlined
designs that overcome these limitations. In particular, we first propose a
Layer Feature-Enhanced module (LFE) to encode the graphical layer information
into the primitive feature, which significantly boosts the performance. We also
design a Position-Guided Training (PGT) method to make it easier to learn,
which accelerates the convergence of the model in the early stages and further
promotes performance. Extensive experiments show that our model achieves better
performance and faster convergence than its predecessor SymPoint on the public
benchmark. Our code and trained models are available at
https://github.com/nicehuster/SymPointV2.
