TY  - JOUR
AU  - Farshian, Anis
AU  - Götz, Markus
AU  - Cavallaro, Gabriele
AU  - Debus, Charlotte
AU  - Nießner, Matthias
AU  - Benediktsson, Jón Atli
AU  - Streit, Achim
TI  - Deep-Learning-Based 3-D Surface Reconstruction—A Survey
JO  - Proceedings of the IEEE
VL  - 111
IS  - 11
SN  - 0018-9219
M1  - FZJ-2023-04458
SP  -  1464 - 1501
PY  - 2023
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:001103912800001
DO  - DOI:10.1109/JPROC.2023.3321433
UR  - https://juser.fz-juelich.de/record/1017988
ER  -