  Reconstructing geometry and topology structures from raw unstructured data
has always been an important research topic in indoor mapping research. In this
paper, we aim to reconstruct the floorplan with a vectorized representation
from point clouds. Despite significant advancements achieved in recent years,
current methods still encounter several challenges, such as missing corners or
edges, inaccuracies in corner positions or angles, self-intersecting or
overlapping polygons, and potentially implausible topology. To tackle these
challenges, we present PolyRoom, a room-aware Transformer that leverages
uniform sampling representation, room-aware query initialization, and
room-aware self-attention for floorplan reconstruction. Specifically, we adopt
a uniform sampling floorplan representation to enable dense supervision during
training and effective utilization of angle information. Additionally, we
propose a room-aware query initialization scheme to prevent non-polygonal
sequences and introduce room-aware self-attention to enhance memory efficiency
and model performance. Experimental results on two widely used datasets
demonstrate that PolyRoom surpasses current state-of-the-art methods both
quantitatively and qualitatively. Our code is available at:
https://github.com/3dv-casia/PolyRoom/.
