WS-NeRF: A Mamba-Driven World-State-Aware Adaptive Deblurring Neural Radiance Field

2026-09-18

AI Summary

为解决NeRF在模糊图像输入下的重建质量问题，提出WS-NeRF方法，通过动态调整去模糊先验的置信度，提高了3D重建质量和稳定性。

Abstract

Neural Radiance Fields (NeRF) have attracted extensive attention in recent years due to their strong capability for high-quality 3D reconstruction and novel view synthesis from multi-view images. Existing methods usually rely on high-quality sharp inputs, while real-world image acquisition is highly susceptible to blur degradation, which severely affects the reconstruction quality of NeRF. In this paper, we propose a novel Mamba-driven world-state-aware adaptive deblurring neural radiance field, termed WS-NeRF, to address image degradation and 3D inconsistency. We formulate the alternating optimization of radiance fields as a dynamic evolution process with temporal memory, and jointly exploit comprehensive multi-dimensional world states and a mixture-of-experts mechanism to dynamically adjust the confidence of deblurring priors. Experimental results show that WS-NeRF significantly improves blurry radiance field reconstruction quality, achieving better performance on PSNR, SSIM, and LPIPS, while exhibiting more stable iterative recovery behavior.

Problem

• NeRF
• deblurring
• image degradation
• 3D reconstruction
• novel view synthesis

Innovation

• Mamba-driven
• world-state-aware
• adaptive deblurring
• neural radiance field
• dynamic evolution process