Reconstructing Heritage Interiors from Point Clouds via Architectural Element Segmentation

Indoor 3D reconstruction is crucial for digital documentation, preservation, and management of historical buildings by capturing their detailed spatial layouts and architectural structures. However, reconstructing heritage interiors from point clouds remains challenging due to data incompleteness and imperfect segmentation led by complex geometries, occlusions, and intricate spatial arrangements. To address these challenges, we proposed a semantically guided framework for structured 3D reconstruction of historical interiors. Our approach began by extracting architectural elements using learning-based semantic and instance segmentation, augmented by an implicit feature enhancement module. We then generated a 2D floor plan from wall and ceiling points, followed by constructing an enclosure model composed of walls, ceilings, and floors via Markov random field optimization. Remaining structural elements (e.g., beams, columns, windows, and doors) were reconstructed from detected geometric primitives. Finally, we integrated the enclosure model and structural element models into a complete indoor reconstruction. Experiments on point clouds captured by mobile laser scanning, terrestrial laser scanning, and photogrammetry demonstrated that our method achieved superior accuracy, compactness, and computational efficiency compared to existing approaches, validating its robustness across diverse data sources.