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Stefan cel Mare
University of Suceava
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Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  3/2024 - 1
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Semantic Segmentation and Reconstruction of Indoor Scene Point Clouds

HAO, W. See more information about HAO, W. on SCOPUS See more information about HAO, W. on IEEExplore See more information about HAO, W. on Web of Science, WEI, H. See more information about  WEI, H. on SCOPUS See more information about  WEI, H. on SCOPUS See more information about WEI, H. on Web of Science, WANG, Y. See more information about WANG, Y. on SCOPUS See more information about WANG, Y. on SCOPUS See more information about WANG, Y. on Web of Science
 
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Download PDF pdficon (4,600 KB) | Citation | Downloads: 502 | Views: 511

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
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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.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 9,815 TCR
SCOPUS® Citations for all references: 23,608 TCR

Web of Science® Average Citations per reference: 252 ACR
SCOPUS® Average Citations per reference: 605 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-11-09 03:23 in 254 seconds.




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