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Deep-Learning-Based 3-D Surface Reconstruction—A Survey

Farshian, Anis 1; Götz, Markus ORCID iD icon 1; Cavallaro, Gabriele; Debus, Charlotte 1; Nießner, Matthias; Benediktsson, Jón Atli; Streit, Achim ORCID iD icon 1
1 Scientific Computing Center (SCC), Karlsruher Institut für Technologie (KIT)

Abstract:

In the last decade, deep learning (DL) has significantly impacted industry and science. Initially largely motivated by computer vision tasks in 2-D imagery, the focus has shifted toward 3-D data analysis. In particular, 3-D surface reconstruction, i.e., reconstructing a 3-D shape from sparse input, is of great interest to a large variety of application fields. DL-based approaches show promising quantitative and qualitative surface reconstruction performance compared to traditional computer vision and geometric algorithms. This survey provides a comprehensive overview of these DL-based methods for 3-D surface reconstruction. To this end, we will first discuss input data modalities, such as volumetric data, point clouds, and RGB, single-view, multiview, and depth images, along with corresponding acquisition technologies and common benchmark datasets. For practical purposes, we also discuss evaluation metrics enabling us to judge the reconstructive performance of different methods. The main part of the document will introduce a methodological taxonomy ranging from point- and mesh-based techniques to volumetric and implicit neural approaches. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000165735
Veröffentlicht am 22.12.2023
Originalveröffentlichung
DOI: 10.1109/JPROC.2023.3321433
Scopus
Zitationen: 4
Web of Science
Zitationen: 3
Dimensions
Zitationen: 7
Cover der Publikation
Zugehörige Institution(en) am KIT Scientific Computing Center (SCC)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 11.2023
Sprache Englisch
Identifikator ISSN: 0018-9219, 1558-2256
KITopen-ID: 1000165735
HGF-Programm 46.21.04 (POF IV, LK 01) HAICU
Weitere HGF-Programme 46.21.02 (POF IV, LK 01) Cross-Domain ATMLs and Research Groups
Erschienen in Proceedings of the IEEE
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Band 111
Heft 11
Seiten 1464 – 1501
Vorab online veröffentlicht am 17.11.2023
Nachgewiesen in Web of Science
Scopus
Dimensions
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