The proposed regularization framework in Simple-RF can be extended to other radiance field models beyond NeRF, TensoRF, and ZipNeRF by following a similar approach of identifying the components of the model that lead to overfitting with sparse input views and designing augmentations to reduce the model's capability in those areas.
For different radiance field models, the specific components causing overfitting may vary, so a thorough analysis of the model's behavior with sparse input views is necessary. Once the problematic components are identified, augmentations can be designed to constrain the model's learning in those areas. By reducing the model's capability in these aspects, simpler solutions can be encouraged, leading to better generalization and performance with sparse input views.
Additionally, the depth supervision approach used in Simple-RF can be adapted and tailored to suit the specific characteristics and requirements of different radiance field models. By customizing the depth supervision strategy based on the model's architecture and behavior, the regularization framework can be effectively applied to a wide range of radiance field models, extending its benefits to various scenarios and datasets.