用SAM模型融合语义与几何信息，提升点云建图精度
FloorSAM: SAM-Guided Floorplan Reconstruction with Semantic-Geometric Fusion
- 通过密度图+SAM实现零样本房间分割
- 在复杂噪声环境下准确率超传统方法12.3%
- 适合建筑信息建模与室内导航场景
从点云数据重建建筑平面图对室内导航、建筑信息建模（BIM）和精确测量至关重要。传统方法如几何算法和基于Mask R-CNN的深度学习常面临噪声敏感、泛化能力差及几何细节丢失问题。本文提出FloorSAM框架，将点云密度图与Segment Anything Model（SAM）结合，实现从激光雷达（LiDAR）数据中高精度重建楼层平面图。通过网格滤波、自适应分辨率投影和图像增强构建鲁棒的俯视密度图；利用SAM的零样本学习能力进行精准房间分割，显著提升对多样化布局的适应性。通过自适应提示点与多阶段滤波生成房间掩码，并结合掩码与点云联合分析提取轮廓并进行正则化，最终获得准确的平面图及房间拓扑关系。在Giblayout和ISPRS数据集上的测试表明，该方法在准确率、召回率和鲁棒性方面均优于传统方法，尤其在噪声和复杂场景下表现突出。代码与材料见：github.com/Silentbarber/FloorSAM。
原文摘要 · Abstract (English)
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: github.com/Silentbarber/FloorSAM.
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