FloorSAM: SAM-Guided Floorplan Reconstruction with Semantic-Geometric Fusion
This addresses the problem of accurate indoor mapping for applications like navigation and BIM, though it is incremental as it builds on existing segmentation models.
The paper tackles floor plan reconstruction from LiDAR point clouds by proposing FloorSAM, a framework that integrates point cloud density maps with the Segment Anything Model (SAM) for room segmentation, resulting in improved accuracy and robustness on datasets like Giblayout and ISPRS.
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.