Fast and high quality neural radiance fields reconstruction based on depth regularization

Bin Zhu; Gaoxiang He; Bo Xie; Yi Chen; Yaoxuan Zhu; Liuying Chen

doi:10.1117/12.3014528

9 January 2024 Fast and high quality neural radiance fields reconstruction based on depth regularization

Bin Zhu, Gaoxiang He, Bo Xie, Yi Chen, Yaoxuan Zhu, Liuying Chen

Proceedings Volume 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023); 129692F (2024) https://doi.org/10.1117/12.3014528
Event: International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023), 2023, Qingdao, China

Abstract

Although the Neural Radiance Fields (NeRF) has been shown to achieve high-quality novel view synthesis, existing models still perform poorly in some scenarios, particularly unbounded scenes. These models either require excessively long training times or produce suboptimal synthesis results. Consequently, we propose SD-NeRF, which consists of a compact neural radiance field model and self-supervised depth regularization. Experimental results demonstrate that SDNeRF can shorten training time by over 20 times compared to Mip-NeRF360 without compromising reconstruction accuracy.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Bin Zhu, Gaoxiang He, Bo Xie, Yi Chen, Yaoxuan Zhu, and Liuying Chen "Fast and high quality neural radiance fields reconstruction based on depth regularization", Proc. SPIE 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023), 129692F (9 January 2024); https://doi.org/10.1117/12.3014528

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