JNeRF: An efficient heterogeneous NeRF model zoo based on Jittor

Guo-Wei Yang; Zheng-Ning Liu; Dong-Yang Li; Hao-Yang Peng

doi:10.1007/s41095-022-0327-z

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Short Communication | Open Access

JNeRF: An efficient heterogeneous NeRF model zoo based on Jittor

Guo-Wei Yang^¹(), Zheng-Ning Liu^², Dong-Yang Li^¹, Hao-Yang Peng^¹

1 Department of Computer Science and Technology, TsinghuaUniversity, Beijing 100084, China

2 Fitten Tech Co., Ltd., Beijing 100084, China

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References

[1]

Hu,

S. M.

; Liang,

; 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,

; Evans,

; Schied,

; Keller,

Instant neural graphics primitives with a multiresolution hash encoding. arXiv preprint arXiv:2201.05989, 2022.

Crossref Google Scholar

[3]

Paszke,

; Gross,

; Massa,

; Lerer,

; Bradbury,

; Chanan,

; Killeen,

; Lin,

; Gimelshein,

; Antiga,

; 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,

; Srinivasan,

P. P.

; Tancik,

; Barron,

J. T.

; Ramamoorthi,

; Ng,

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