Deep-Learning-Based 3-D Surface Reconstruction—A Survey-Reference-Cited by-同舟云学术

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

Published:2023-11 Issue:11 Volume:111 Page:1464-1501
ISSN:0018-9219
Container-title:Proceedings of the IEEE
language:
Short-container-title:Proc. IEEE

Author:

Farshian Anis¹^ORCID,Götz Markus¹^ORCID,Cavallaro Gabriele²^ORCID,Debus Charlotte¹^ORCID,Nießner Matthias³^ORCID,Benediktsson Jón Atli⁴^ORCID,Streit Achim¹

Affiliation:

1. Steinbuch Centre for Computing, Karlsruhe Institute of Technology, Karlsruhe, Germany

2. Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany

3. Department of Informatics, Visual Computing Laboratory, Technical University of Munich, Munich, Germany

4. Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/5/10323242/10301359.pdf?arnumber=10301359

Reference246 articles.

1. NeRF: Representing scenes as neural radiance fields for view synthesis;mildenhall;Proc Eur Conf Comput Vis,2020

2. 3D Equivariant Graph Implicit Functions

3. Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion

4. Attention is all you need;vaswani;Proc Adv Neural Inf Process Syst,2017

5. DONeRF: Towards Real‐Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks

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