The Neural Radiance Field, or NeRF, has emerged as a powerful tool for 3D reconstruction from multiple images. While recent advancements have shown promising results in editing reconstructed NeRFs using diffusion priors, there are still challenges to overcome, especially in synthesizing coherent geometry in uncovered areas.

One significant challenge lies in the high diversity of synthetic content generated by diffusion models. This diversity can make it difficult for the radiance field to converge to clear and consistent geometry. Additionally, when applied to real-world data, latent diffusion models often introduce incoherent textural shifts, largely due to auto-encoding errors. These issues are further complicated by the use of pixel-distance losses, which can be detrimental in this context.

To address these problems, researchers have proposed a novel framework that involves tempering the diffusion model’s stochasticity with per-scene customization. This customization helps to mitigate the impact of diversity and guide the model towards more coherent results. Additionally, masked adversarial training is employed to reduce incoherent textural shifts, enhancing the model’s ability to align with image conditions.

Through rigorous experimentation, this framework has achieved state-of-the-art results in NeRF inpainting across various real-world scenes. The discovery that commonly used pixel and perceptual losses hinder the NeRF inpainting task is particularly intriguing, highlighting the importance of tailored loss functions for specific tasks.

This paper showcases the ongoing advancements in 3D reconstruction and the potential of NeRF-based methods to push the boundaries of what is possible. By addressing challenges related to geometry synthesis and textural coherence, the proposed framework takes us a step closer to more robust and versatile NeRF editing and inpainting.