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

Hang Jiang; Jinghao Wang; Yiming Zhang; Xinhong Wang; Luwei Ran; Yinfeng Yu

eess.IVSep 18, 2026

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

Authors: Hang Jiang, Jinghao Wang, Yiming Zhang, Xinhong Wang, Luwei Ran, Yinfeng Yu

Organizations: School of Intelligent Science and Technology, Xinjiang University, Urumqi, China · School of Computer Science and Technology, Xinjiang University, Urumqi, China · Joint Research Laboratory for Embodied Intelligence, Xinjiang University, Urumqi, China · Joint International Research Laboratory of Silk Road Multilingual Cognitive, Xinjiang University, Urumqi, China

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.

Figures & tables

Fig. 1: Overview of the blur-aware radiance-field reconstruction task. Blurry input images are used to optimize the radiance field and synthesize restored novel views.

Fig. 2: Overall framework of WS-NeRF. The world-state observer, Mamba-driven temporal evolution process, and adaptive controller with mixture-of-experts jointly regulate prior guidance during radiance-field optimization.

Fig. 3: Dataset examples used in the experiments, including synthetic motion blur, synthetic defocus blur, and real motion-blur scenes.

Model	Camera Motion Blur			Defocus Blur			BlurRF-Real		BlurRF-SB
Model	PSNR ( $\uparrow$ )	SSIM ( $\uparrow$ )	LPIPS ( $\downarrow$ )	PSNR ( $\uparrow$ )	SSIM ( $\uparrow$ )	LPIPS ( $\downarrow$ )	NIQE ( $\downarrow$ )	ARNIQA ( $\uparrow$ )	PSNR ( $\uparrow$ )	SSIM ( $\uparrow$ )	LPIPS ( $\downarrow$ )
Deblur-NeRF	27.67	0.8340	0.1450	30.03	0.8727	0.1137	6.341	0.279	25.43	0.7264	0.2319
BAD-NeRF	21.74	0.5298	0.3969	–	–	–	9.908	0.237	21.97	0.5231	0.4731
DP-NeRF	28.03	0.8412	0.1267	30.15	0.8763	0.0991	6.498	0.266	27.07	0.7940	0.1795
ExBluRF	26.56	0.7823	0.1955	–	–	–	7.479	0.279	24.16	0.6649	0.2659
PDRF-10	28.33	0.8435	0.1495	30.03	0.8750	0.1225	6.243	0.266	26.66	0.7730	0.2124

TABLE I: Quantitative results of WS-NeRF and competing methods across all benchmarks. The best metric in each column is shown in bold.

Variant	Camera Motion Blur			Defocus Blur			BlurRF-SB
Variant	PSNR ( $\uparrow$ )	SSIM ( $\uparrow$ )	LPIPS ( $\downarrow$ )	PSNR ( $\uparrow$ )	SSIM ( $\uparrow$ )	LPIPS ( $\downarrow$ )	PSNR ( $\uparrow$ )	SSIM ( $\uparrow$ )	LPIPS ( $\downarrow$ )
w/o World State	29.84	0.8687	0.1164	32.55	0.9048	0.0967	29.26	0.8507	0.1498
w/o Mamba	29.91	0.8672	0.1159	32.44	0.9051	0.0969	29.05	0.8502	0.1507
w/o Multi-strategy Fusion	29.93	0.8663	0.1163	32.49	0.9054	0.0976	29.06	0.8489	0.1516
Full Model	30.04	0.8699	0.1152	32.50	0.9046	0.0966	29.24	0.8519	0.1496

TABLE II: Ablation study of WS-NeRF. The best metric in each column is shown in bold.

Fig. 4: Qualitative comparison on synthetic and real blurry radiance-field benchmarks. WS-NeRF restores sharper structures while preserving cross-view geometry.

Fig. 5: Early-stage failure case in alternating radiance-field restoration. Unreliable rendered guidance can introduce artifacts and motivate state-aware adaptive control.

Neural Radiance Fields

Neural radiance fields represent 3D scenes as continuous functions of position and viewing direction to synthesize images from new camera views.

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DeblurNVS: Geometric Latent Diffusion for Novel View Synthesis from Sparse Motion-Blurred Images

Changyue Shi, Wangbo Yu, Chaoran Feng, Li Yuan

School of AI for Science, Peking University Shenzhen Graduate School, Shenzhen, 518055 China · School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen, 518055 China

Latent Diffusion Models 3D Reconstruction Novel View Synthesis

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Novel view synthesis (NVS) is a fundamental problem in computer vision and graphics. Recent advances in neural radiance fields (NeRF), 3D Gaussian Splatting (3DGS), and generative view synthesis have substantially improved its quality. Yet most methods still rely on clean observations, where image structures and cross-view geometric cues are well preserved. Motion blur breaks this assumption by corrupting local details and weakening multi-view correspondences. Such blur commonly arises from camera shake, scene motion, or finite exposure in practical capture. Blur-aware NVS methods address this degradation by modeling image formation, but their reliance on costly per-scene optimization limits efficient and generalizable sparse-view synthesis. To address this, we propose DeblurNVS, a novel framework for synthesizing high-fidelity novel views directly from sparse motion-blurred images, without requiring per-scene optimization. DeblurNVS restores the intermediate geometric representations needed for multi-view reasoning, enabling blurred inputs to recover reliable structure and correspondence cues. The restored representations are then combined with target camera information to synthesize the target-view representation and reconstruct a sharp RGB novel view. To enable the large-scale training, we construct a motion-blurred NVS dataset from DL3DV-10K using interpolation-based finite-exposure blur synthesis. Extensive experiments demonstrate that DeblurNVS outperforms existing baselines on synthetic motion-blur benchmarks and generalizes to real motion-blurred scenes, producing perceptually sharper and structurally more stable novel views while avoiding costly per-scene optimization. Project page: https://github.com/PKU-YuanGroup/DeblurNVS.

Abstract

Neural Radiance Fields

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