SSNeRF: Sparse View Semi-supervised Neural Radiance Fields with Augmentation
<|reference_start|>SSNeRF: Sparse View Semi-supervised Neural Radiance Fields with Augmentation: Sparse view NeRF is challenging because limited input images lead to an under constrained optimization problem for volume rendering. Existing methods address this issue by relying on supplementary information, such as depth maps. However, generating this supplementary information accurately remains problematic and often leads to NeRF producing images with undesired artifacts. To address these artifacts and enhance robustness, we propose SSNeRF, a sparse view semi supervised NeRF method based on a teacher student framework. Our key idea is to challenge the NeRF module with progressively severe sparse view degradation while providing high confidence pseudo labels. This approach helps the NeRF model become aware of noise and incomplete information associated with sparse views, thus improving its robustness. The novelty of SSNeRF lies in its sparse view specific augmentations and semi supervised learning mechanism. In this approach, the teacher NeRF generates novel views along with confidence scores, while the student NeRF, perturbed by the augmented input, learns from the high confidence pseudo labels. Our sparse view degradation augmentation progressively injects noise into volume rendering weights, perturbs feature maps in vulnerable layers, and simulates sparse view blurriness. These augmentation strategies force the student NeRF to recognize degradation and produce clearer rendered views. By transferring the student's parameters to the teacher, the teacher gains increased robustness in subsequent training iterations. Extensive experiments demonstrate the effectiveness of our SSNeRF in generating novel views with less sparse view degradation. We will release code upon acceptance.<|reference_end|>
@article{cao2024ssnerf:,
    title={SSNeRF: Sparse View Semi-supervised Neural Radiance Fields with
  Augmentation},
    author={Xiao Cao, Beibei Lin, Bo Wang, Zhiyong Huang, Robby T. Tan},
    journal={arXiv preprint arXiv:2408.09144},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09144},
    primaryClass={cs.CV}
}