  In this paper, we introduce a novel method called FRI-Net for 2D floorplan
reconstruction from 3D point cloud. Existing methods typically rely on corner
regression or box regression, which lack consideration for the global shapes of
rooms. To address these issues, we propose a novel approach using a room-wise
implicit representation with structural regularization to characterize the
shapes of rooms in floorplans. By incorporating geometric priors of room
layouts in floorplans into our training strategy, the generated room polygons
are more geometrically regular. We have conducted experiments on two
challenging datasets, Structured3D and SceneCAD. Our method demonstrates
improved performance compared to state-of-the-art methods, validating the
effectiveness of our proposed representation for floorplan reconstruction.
