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Computational Visual Media 2023, 9(2): 401-404 https://doi.org/10.1007/s41095-022-0327-z
Short Communication | Open Access | Issue | Published: 03 January 2023

JNeRF: An efficient heterogeneous NeRF model zoo based on Jittor

Show Author's Information Guo-Wei Yang1( ),  Zheng-Ning Liu2,  Dong-Yang Li1,  Hao-Yang Peng1
Department of Computer Science and Technology, TsinghuaUniversity, Beijing 100084, China
Fitten Tech Co., Ltd., Beijing 100084, China
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
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JNeRF: An efficient heterogeneous NeRF model zoo based on Jittor

Show Author's information Guo-Wei Yang1( ), Zheng-Ning Liu2, Dong-Yang Li1, Hao-Yang Peng1
Department of Computer Science and Technology, TsinghuaUniversity, Beijing 100084, China
Fitten Tech Co., Ltd., Beijing 100084, China

References(4)

[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.
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Müller, T.; Evans, A.; Schied, C.; Keller, A. Instant neural graphics primitives with a multiresolution hash encoding. arXiv preprint arXiv:2201.05989, 2022.
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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.
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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.
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Publication history

Received: 19 September 2022
Accepted: 21 November 2022
Published: 03 January 2023
Issue date: June 2023

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© The Author(s) 2022.

Acknowledgements

This paper was supported by National Key R&D Program of China (Project No. 2021ZD0112902)

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