Sparse-DeRF: Deblurred Neural Radiance Fields from Sparse View
<|reference_start|>Sparse-DeRF: Deblurred Neural Radiance Fields from Sparse View: Recent studies construct deblurred neural radiance fields (DeRF) using dozens of blurry images, which are not practical scenarios if only a limited number of blurry images are available. This paper focuses on constructing DeRF from sparse-view for more pragmatic real-world scenarios. As observed in our experiments, establishing DeRF from sparse views proves to be a more challenging problem due to the inherent complexity arising from the simultaneous optimization of blur kernels and NeRF from sparse view. Sparse-DeRF successfully regularizes the complicated joint optimization, presenting alleviated overfitting artifacts and enhanced quality on radiance fields. The regularization consists of three key components: Surface smoothness, helps the model accurately predict the scene structure utilizing unseen and additional hidden rays derived from the blur kernel based on statistical tendencies of real-world; Modulated gradient scaling, helps the model adjust the amount of the backpropagated gradient according to the arrangements of scene objects; Perceptual distillation improves the perceptual quality by overcoming the ill-posed multi-view inconsistency of image deblurring and distilling the pre-filtered information, compensating for the lack of clean information in blurry images. We demonstrate the effectiveness of the Sparse-DeRF with extensive quantitative and qualitative experimental results by training DeRF from 2-view, 4-view, and 6-view blurry images.<|reference_end|>
@article{lee2024sparse-derf:,
    title={Sparse-DeRF: Deblurred Neural Radiance Fields from Sparse View},
    author={Dogyoon Lee, Donghyeong Kim, Jungho Lee, Minhyeok Lee, Seunghoon Lee,
  and Sangyoun Lee},
    journal={arXiv preprint arXiv:2407.06613},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06613},
    primaryClass={cs.CV}
}