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.

翻译：本文提出了一种名为FRI-Net的新方法，用于从三维点云重建二维平面图。现有方法通常依赖于角点回归或边界框回归，缺乏对房间全局形状的考量。为解决这些问题，我们提出了一种采用房间级隐式表示并结合结构正则化的新方法，以刻画平面图中房间的形状。通过将平面图中房间布局的几何先验知识融入训练策略，所生成的房间多边形在几何上更具规则性。我们在Structured3D和SceneCAD两个具有挑战性的数据集上进行了实验。与现有先进方法相比，我们的方法展现出更优的性能，验证了所提表示方法在平面图重建中的有效性。