WS-NeRF: A Mamba-Driven World-State-Aware Adaptive Deblurring Neural Radiance Field
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 exp...
| Authors | Hang Jiang, Jinghao Wang, Yiming Zhang et al. |
|---|---|
| Primary category | eess.IV |
| Published | 18 September 2026 |
| Last updated at source | 18 September 2026 |
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