While relying solely on Neural Radiance Fields (NeRF) as the scene representation in visual localization offers many advantages like compactness and realism, there are also limitations to consider:
Scalability: Training large-scale scenes with NeRF may require significant computational resources due to the complexity of capturing all details accurately.
Generalization: Pre-trained NeRF models might struggle with generalizing across different scenes or environments without additional training or modifications.
Real-time Processing: The computation-intensive nature of rendering images from a full-fledged NeRF model could hinder real-time performance for certain applications.
Limited Interpretation: While NeRF features are effective for matching tasks, they may lack interpretability compared to explicit representations like point clouds or meshes.
To address these limitations, researchers need to explore techniques for improving scalability through efficient training methods, enhancing generalization capabilities through transfer learning approaches, optimizing algorithms for real-time processing speed without compromising accuracy.