MVIP-NeRF: Multi-view 3D Inpainting on NeRF Scenes via Diffusion Prior
Honghua Chen, Chen Change Loy, Xingang Pan

TL;DR
MVIP-NeRF introduces a diffusion prior-based multi-view inpainting method for NeRF scenes, achieving consistent appearance and geometry completion by jointly optimizing across views with iterative score distillation sampling.
Contribution
It presents a novel multi-view inpainting framework using diffusion priors and score distillation sampling to improve consistency and geometry accuracy in NeRF scene inpainting.
Findings
Outperforms previous NeRF inpainting methods in appearance recovery.
Achieves superior geometry completion and alignment.
Ensures consistent multi-view inpainting across large view variations.
Abstract
Despite the emergence of successful NeRF inpainting methods built upon explicit RGB and depth 2D inpainting supervisions, these methods are inherently constrained by the capabilities of their underlying 2D inpainters. This is due to two key reasons: (i) independently inpainting constituent images results in view-inconsistent imagery, and (ii) 2D inpainters struggle to ensure high-quality geometry completion and alignment with inpainted RGB images. To overcome these limitations, we propose a novel approach called MVIP-NeRF that harnesses the potential of diffusion priors for NeRF inpainting, addressing both appearance and geometry aspects. MVIP-NeRF performs joint inpainting across multiple views to reach a consistent solution, which is achieved via an iterative optimization process based on Score Distillation Sampling (SDS). Apart from recovering the rendered RGB images, we also…
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Taxonomy
TopicsOptical measurement and interference techniques · Computer Graphics and Visualization Techniques · Industrial Vision Systems and Defect Detection
MethodsInpainting · Diffusion
