Semantic Segmentation and Reconstruction of Indoor Scene Point Clouds

Abstract

Automatic 3D reconstruction of indoor scenes remains a challenging task due to the incomplete and noisy nature of scanned data. We propose a semantic-guided method for reconstructing indoor scene based on semantic segmentation of point clouds. Firstly, a Multi-Feature Adaptive Aggregation Network is designed for semantic segmentation, assigning the semantic label for each point. Then, a novel slicing-projection method is proposed to segment and reconstruct the walls. Next, a hierarchical Euclidean Clustering is proposed to separate objects into individual ones. Finally, each object is replaced with the most similar CAD model from the database, utilizing the Rotational Projection Statistics (RoPS) descriptor and the iterative closest point (ICP) algorithm. The selected template models are then deformed and transformed to fit the objects in the scene. Experimental results demonstrate that the proposed method achieves high-quality reconstruction even when faced with defective scanned point clouds.