Improving Neural Radiance Field Inpainting with Masked Adversarial Learning and Latent Diffusion Customization

Grunnleggende konsepter

The proposed framework leverages a latent diffusion model and masked adversarial training to improve the quality and consistency of neural radiance field (NeRF) inpainting, achieving state-of-the-art performance on various real-world scenes.

Sammendrag

The paper presents a method for improving neural radiance field (NeRF) inpainting by addressing two key challenges:

The high diversity and randomness of the latent diffusion model used for 2D image inpainting leads to 3D inconsistency in the final NeRF.
The textural shift between the inpainted and reconstructed regions in the 2D images causes artifacts in the NeRF.

To address these issues, the authors propose the following:

Masked Adversarial Training: The authors design a masked adversarial training scheme to supervise the NeRF in the inpainting regions. This promotes high-frequency details without relying on pixel-level consistency.
Per-Scene Customization: The authors fine-tune the latent diffusion model for each scene, encouraging it to generate contents that are more coherent to the reconstructed scene.
Iterative Dataset Update: The authors gradually update the inpainting region in the training dataset to leverage the 3D consistency of the NeRF rendering.

The authors conduct extensive experiments on two real-world NeRF inpainting benchmarks, demonstrating that their proposed MALD-NeRF framework outperforms state-of-the-art methods in both qualitative and quantitative evaluations.

Statistikk

"Given a set of posed images associated with inpainting masks, the proposed framework estimates a NeRF that renders high-quality novel views, where the inpainting region is realistic and contains high-frequency details."
"Our framework yields state-of-the-art NeRF inpainting results on various real-world scenes."

Sitater

"Leveraging 2D latent diffusion models for NeRF inpainting is challenging for two reasons. First, the input images inpainted by the 2D latent diffusion model are not 3D consistent. The issue leads to blurry and mist-alike results in the inpainting region if pixel-level objectives (i.e., L1, L2) are used during NeRF optimization. Second, the pixels inpainted by the latent diffusion model typically showcase a texture shift compared to the observed pixels in the input image."

Viktige innsikter hentet fra

Taming Latent Diffusion Model for Neural Radiance Field Inpainting

by Chieh Hubert... klokken arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09995.pdf

Dypere Spørsmål

How could the proposed framework be extended to handle even larger inpainting regions or more complex scene geometries

To handle larger inpainting regions or more complex scene geometries, the proposed framework could be extended in several ways:

Hierarchical Inpainting: Implement a hierarchical inpainting approach where the scene is divided into smaller regions, and each region is inpainted separately. This would allow for more detailed and accurate inpainting of larger areas.

Progressive Inpainting: Utilize a progressive inpainting strategy where the inpainting process starts with rough estimates and gradually refines the details. This can help handle larger regions by breaking down the inpainting task into manageable steps.

Multi-scale Representation: Incorporate a multi-scale representation in the neural network architecture to capture details at different levels of granularity. This can help in inpainting complex scene geometries with varying levels of detail.

Attention Mechanisms: Integrate attention mechanisms into the network to focus on specific regions of interest during inpainting. This can improve the network's ability to handle complex geometries by selectively attending to important features.

What are the potential limitations of using a masked adversarial training scheme, and how could they be addressed in future work

The masked adversarial training scheme has potential limitations that could be addressed in future work:

Boundary Artifacts: The scheme may still result in texture discrepancies or artifacts at the boundaries between inpainted and real regions. This could be mitigated by refining the masking strategy or incorporating additional loss terms to encourage smoother transitions.

Training Instability: Adversarial training can sometimes lead to training instability or mode collapse. Techniques such as spectral normalization or gradient penalty could be employed to stabilize the training process.

Generalization: The masked adversarial training may struggle to generalize to unseen or highly diverse scenes. Transfer learning or domain adaptation techniques could be explored to improve generalization capabilities.

Could the per-scene customization approach be applied to other generative models beyond latent diffusion to further improve NeRF inpainting performance

The per-scene customization approach could be applied to other generative models beyond latent diffusion to enhance NeRF inpainting performance:

Variational Autoencoders (VAEs): By customizing the latent space of VAEs based on scene-specific tokens, the model can learn to generate more coherent and contextually relevant inpainting results.

Generative Adversarial Networks (GANs): Tailoring the GAN architecture and training process to incorporate scene-specific information can improve the quality and consistency of inpainted NeRFs.

Transformers: Adapting the transformer architecture with per-scene customization can enable the model to capture long-range dependencies and contextual information for more accurate NeRF inpainting.

By applying per-scene customization to a variety of generative models, the inpainting process can be optimized to produce high-quality and realistic results across different scenes and scenarios.