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Semantic Segmentation and Reconstruction of Indoor Scene Point CloudsHAO, W. , WEI, H. , WANG, Y. |
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Author keywords
point clouds, semantic segmentation, indoor scene reconstruction, slicing-projection method, template matching
References keywords
point(28), vision(15), clouds(14), semantic(13), reconstruction(13), recognition(13), indoor(13), segmentation(12), cloud(12), pattern(11)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2024-08-31
Volume 24, Issue 3, Year 2024, On page(s): 3 - 12
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2024.03001
Web of Science Accession Number: 001306111400001
SCOPUS ID: 85203023424
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. |
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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