发表机构
Xinjiang University（新疆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对NeRF在模糊输入下重建质量退化的问题，提出WS-NeRF，利用Mamba驱动的时间记忆动态建模与混合专家机制自适应调整去模糊先验，在PSNR、SSIM和LPIPS上显著提升重建质量并保持稳定迭代。
AI 中文摘要
神经辐射场（Neural Radiance Fields, NeRF）近年来因其从多视图图像进行高质量三维重建和新视角合成的强大能力而受到广泛关注。现有方法通常依赖于高质量的清晰输入，而现实世界中的图像采集极易受到模糊退化的影响，这严重影响了NeRF的重建质量。在本文中，我们提出了一种新颖的基于Mamba驱动的世界状态感知自适应去模糊神经辐射场，称为WS-NeRF，以解决图像退化与三维不一致性问题。我们将辐射场的交替优化过程建模为具有时间记忆的动态演化过程，并联合利用全面的多维世界状态和混合专家机制，动态调整去模糊先验的置信度。实验结果表明，WS-NeRF显著提升了模糊辐射场的重建质量，在PSNR、SSIM和LPIPS指标上取得了更优的性能，同时展现出更稳定的迭代恢复行为。
英文摘要
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.
CommentsMain paper (6 pages). Accepted for publication by IEEE International Conference on Systems, Man, and Cybernetics 2026 (IEEE SMC 2026)
