AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Computational Visual Media Article
PDF (2.5 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Short Communication | Open Access

JNeRF: An efficient heterogeneous NeRF model zoo based on Jittor

Department of Computer Science and Technology, TsinghuaUniversity, Beijing 100084, China
Fitten Tech Co., Ltd., Beijing 100084, China
Show Author Information

Graphical Abstract

References

[1]
Hu, S. M.; Liang, D.; Yang, G. Y.; Yang, G. W.; Zhou, W. Y. Jittor: A novel deep learning framework with meta-operators and unified graph execution. Science China Information Sciences Vol. 63, No. 12, Article No. 222103, 2020.
Crossref Google Scholar
[2]
Müller, T.; Evans, A.; Schied, C.; Keller, A. Instant neural graphics primitives with a multiresolution hash encoding. arXiv preprint arXiv:2201.05989, 2022.
Crossref Google Scholar
[3]
Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; et al. PyTorch: An imperative style, high-performance deep learning library. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems, Article No. 721, 8026–8037, 2019.
[4]
Mildenhall, B.; Srinivasan, P. P.; Tancik, M.; Barron, J. T.; Ramamoorthi, R.; Ng, R. NeRF. Communications of the ACM Vol. 65, No. 1, 99–106, 2022.
Crossref Google Scholar
Computational Visual Media
Volume 9 Issue 2,
June 2023
Pages 401-404
DOI: 10.1007/s41095-022-0327-z
Cite this article:
Yang G-W, Liu Z-N, Li D-Y, et al. JNeRF: An efficient heterogeneous NeRF model zoo based on Jittor. Computational Visual Media, 2023, 9(2): 401-404. https://doi.org/10.1007/s41095-022-0327-z

7517

Views

53

Downloads

4

Crossref

5

Web of Science

4

Scopus

0

CSCD

Altmetrics
Received: 19 September 2022
Accepted: 21 November 2022
Published: 03 January 2023
© The Author(s) 2022.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduc-tion in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Other papers from this open access journal are available free of charge from http://www.springer.com/journal/41095. To submit a manuscript, please go to https://www.editorialmanager.com/cvmj.
Return