Abstract
Reconstructing building floor plans from point cloud data is key for indoor navigation, BIM, and precise measurements. Traditional methods like geometric algorithms and Mask R-CNN-based deep learning often face issues with noise, limited generalization, and loss of geometric details. We propose FloorSAM, a framework that integrates point cloud density maps with the Segment Anything Model (SAM) for accurate floor plan reconstruction from LiDAR data. Using grid-based filtering, adaptive resolution projection, and image enhancement, we create robust top-down density maps. FloorSAM uses SAM's zero-shot learning for precise room segmentation, improving reconstruction across diverse layouts. Room masks are generated via adaptive prompt points and multistage filtering, followed by joint mask and point cloud analysis for contour extraction and regularization. This produces accurate floor plans and recovers room topological relationships. Tests on Giblayout and ISPRS datasets show better accuracy, recall, and robustness than traditional methods, especially in noisy and complex settings. Code and materials: this http URL.
Abstract (translated)
从点云数据重建建筑楼层平面图是室内导航、BIM（建筑信息建模）和精确测量的关键。传统的几何算法及基于Mask R-CNN的深度学习方法在处理噪声问题时通常面临挑战，且存在泛化能力有限和几何细节丢失的问题。我们提出了FloorSAM框架，该框架将点云密度图与分割任何东西模型（Segment Anything Model, SAM）结合，用于从LiDAR数据中准确重建楼层平面图。 通过基于网格的过滤、自适应分辨率投影以及图像增强方法，我们创建了稳健的顶部视角密度图。FloorSAM利用SAM的零样本学习能力进行精确房间分割，在各种布局下提高重建精度。房间掩码是通过自适应提示点和多级过滤生成的，并随后结合掩码与点云分析提取轮廓并进行正则化处理。这种方法产生准确的楼层平面图，同时恢复房间拓扑关系。 在Giblayout和ISPRS数据集上的测试显示，FloorSAM在准确性、召回率及鲁棒性方面优于传统方法，特别是在嘈杂和复杂环境中表现尤为出色。代码与材料可在[此链接](http://this.http.url)获取。
URL
https://arxiv.org/abs/2509.15750
