Drantal-NeRF: Diffusion-Based Restoration for Anti-aliasing Neural Radiance Field

• URL: http://arxiv.org/abs/2407.07461v1
• Date: Wed, 10 Jul 2024 08:32:13 GMT
• Title: Drantal-NeRF: Diffusion-Based Restoration for Anti-aliasing Neural Radiance Field
• Authors: Ganlin Yang, Kaidong Zhang, Jingjing Fu, Dong Liu,
• Abstract summary: Aliasing artifacts in renderings produced by Neural Radiance Field (NeRF) is a long-standing but complex issue. We present a Diffusion-based restoration method for anti-aliasing Neural Radiance Field (Drantal-NeRF)
• Score: 10.225323718645022
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Aliasing artifacts in renderings produced by Neural Radiance Field (NeRF) is a long-standing but complex issue in the field of 3D implicit representation, which arises from a multitude of intricate causes and was mitigated by designing more advanced but complex scene parameterization methods before. In this paper, we present a Diffusion-based restoration method for anti-aliasing Neural Radiance Field (Drantal-NeRF). We consider the anti-aliasing issue from a low-level restoration perspective by viewing aliasing artifacts as a kind of degradation model added to clean ground truths. By leveraging the powerful prior knowledge encapsulated in diffusion model, we could restore the high-realism anti-aliasing renderings conditioned on aliased low-quality counterparts. We further employ a feature-wrapping operation to ensure multi-view restoration consistency and finetune the VAE decoder to better adapt to the scene-specific data distribution. Our proposed method is easy to implement and agnostic to various NeRF backbones. We conduct extensive experiments on challenging large-scale urban scenes as well as unbounded 360-degree scenes and achieve substantial qualitative and quantitative improvements.

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