guest ::
| Home > Publications database > Deep-Learning-Based 3-D Surface Reconstruction—A Survey > print |
| 001 | 1017988 | ||
| 005 | 20240116084322.0 | ||
| 024 | 7 | _ | |a 10.1109/JPROC.2023.3321433 |2 doi |
| 024 | 7 | _ | |a 0018-9219 |2 ISSN |
| 024 | 7 | _ | |a 1558-2256 |2 ISSN |
| 024 | 7 | _ | |a 10.34734/FZJ-2023-04458 |2 datacite_doi |
| 024 | 7 | _ | |a WOS:001103912800001 |2 WOS |
| 037 | _ | _ | |a FZJ-2023-04458 |
| 082 | _ | _ | |a 620 |
| 100 | 1 | _ | |a Farshian, Anis |0 0000-0002-9888-0653 |b 0 |
| 245 | _ | _ | |a Deep-Learning-Based 3-D Surface Reconstruction—A Survey |
| 260 | _ | _ | |c 2023 |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1702456409_27254 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a 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. Recent research trends, both methodological and for applications, are highlighted, pointing toward future developments. |
| 536 | _ | _ | |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5111 |c POF4-511 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Götz, Markus |0 P:(DE-Juel1)162390 |b 1 |
| 700 | 1 | _ | |a Cavallaro, Gabriele |0 P:(DE-Juel1)171343 |b 2 |
| 700 | 1 | _ | |a Debus, Charlotte |0 0000-0002-7156-2022 |b 3 |
| 700 | 1 | _ | |a Nießner, Matthias |0 0000-0001-6093-5199 |b 4 |
| 700 | 1 | _ | |a Benediktsson, Jón Atli |0 0000-0003-0621-9647 |b 5 |
| 700 | 1 | _ | |a Streit, Achim |0 P:(DE-HGF)0 |b 6 |
| 773 | _ | _ | |a 10.1109/JPROC.2023.3321433 |g p. 1 - 38 |0 PERI:(DE-600)2040232-6 |n 11 |p 1464 - 1501 |t Proceedings of the IEEE |v 111 |y 2023 |x 0018-9219 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1017988/files/3D_Reconstruction_Survey.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:1017988 |p openaire |p open_access |p VDB |p driver |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)171343 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5111 |x 0 |
| 914 | 1 | _ | |y 2023 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b P IEEE : 2022 |d 2023-08-22 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2023-08-22 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2023-08-22 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2023-08-22 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2023-08-22 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1160 |2 StatID |b Current Contents - Engineering, Computing and Technology |d 2023-08-22 |
| 915 | _ | _ | |a IF >= 20 |0 StatID:(DE-HGF)9920 |2 StatID |b P IEEE : 2022 |d 2023-08-22 |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
| 980 | 1 | _ | |a FullTexts |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|