NimbusGS: Unified 3D Scene Reconstruction under Hybrid Weather

Yanying Li; Jinyang Li; Shengfeng He; Yangyang Xu; Junyu Dong; Yong Du

cs.CVMar 28, 2026

NimbusGS: Unified 3D Scene Reconstruction under Hybrid Weather

Authors: Yanying Li, Jinyang Li, Shengfeng He, Yangyang Xu, Junyu Dong, Yong Du

Organizations: School of Computer Science and Technology, Ocean University of China · Singapore Management University · Harbin Institute of Technology (Shenzhen) · Sanya Oceanographic Institution, Ocean University of China

Abstract

We present NimbusGS, a unified framework for reconstructing high-quality 3D scenes from degraded multi-view inputs captured under diverse and mixed adverse weather conditions. Unlike existing methods that target specific weather types, NimbusGS addresses the broader challenge of generalization by modeling the dual nature of weather: a continuous, view-consistent medium that attenuates light, and dynamic, view-dependent particles that cause scattering and occlusion. To capture this structure, we decompose degradations into a global transmission field and per-view particulate residuals. The transmission field represents static atmospheric effects shared across views, while the residuals model transient disturbances unique to each input. To enable stable geometry learning under severe visibility degradation, we introduce a geometry-guided gradient scaling mechanism that mitigates gradient imbalance during the self-supervised optimization of 3D Gaussian representations. This physically grounded formulation allows NimbusGS to disentangle complex degradations while preserving scene structure, yielding superior geometry reconstruction and outperforming task-specific methods across diverse and challenging weather conditions. Code is available at https://github.com/lyy-ovo/NimbusGS.

Figures & tables

Figure 1: We propose NimbusGS, a unified framework for 3D reconstruction under diverse and hybrid weather conditions. It jointly addresses continuous medium effects (haze, H), particulate degradations (snow, S; rain, R), and their mixed combinations. Panel (a) presents visual comparisons across weather types, while panel (b) summarizes metric profiles over seven single and hybrid settings.

Figure 2: Overview of NimbusGS. Starting from a geometry initialization, transient particle effects are separated as per-view residuals. CSM estimates an extinction field from which transmission and airlight are derived, blended with the scene rendering and residuals to reproduce the degradations. This self-supervised process guides the Gaussian representation toward a clean and consistent reconstruction.

Method	Bicycle			Garden			Factory			Tanabata			Average
Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
3DGS [ 20 ]	17.19	0.578	0.319	13.33	0.522	0.344	15.51	0.687	0.206	12.25	0.565	0.262	14.57	0.588	0.283
SeaSplat [ 49 ]	18.28	0.588	0.312	15.07	0.676	0.326	12.50	0.792	0.190	16.44	0.779	0.185	15.57	0.709	0.253
WaterSplatting [ 24 ]	18.19	0.593	0.301	15.25	0.655	0.318	16.15	0.814	0.182	15.02	0.716	0.203	16.15	0.695	0.251
Ours	21.84	0.677	0.249	21.04	0.727	0.274	21.16	0.899	0.104	19.12	0.802	0.131	20.79	0.776	0.190

Table 1: Quantitative comparisons on hazy scenes. The best and second-best scores are color-encoded for clarity.

Panel of Figure 3 — Figure 3: Qualitative results on hazy scenes. Best viewed zoomed in.

Method	Crossroad			Square			Sailboat			Yard			Average
Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
DerainNeRF [ 25 ]	23.97	0.623	0.381	22.71	0.737	0.370	23.73	0.805	0.192	22.86	0.765	0.253	23.31	0.732	0.299
RobustSplat [ 12 ]	27.45	0.729	0.308	26.35	0.793	0.319	26.17	0.751	0.340	27.57	0.796	0.297	26.88	0.767	0.316
WeatherGS [ 39 ]	25.32	0.653	0.345	24.87	0.807	0.309	26.52	0.839	0.195	25.57	0.824	0.250	25.57	0.780	0.274
RainyScape [ 34 ]	31.01	0.851	0.154	29.44	0.883	0.152	29.62	0.851	0.147	29.43	0.820	0.166	29.87	0.851	0.154
Ours	32.15	0.893	0.189	32.13	0.914	0.145	33.19	0.921	0.109	32.18	0.889	0.116	32.41	0.904	0.139

Table 2: Quantitative comparisons on rainy scenes. The best and second-best scores are color-encoded for clarity.

Panel of Figure 4 — Figure 4: Qualitative results on rainy scenes. Best viewed zoomed in.

Method	Bicycle			Garden			Factory			Tanabata			Average
Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
DerainNeRF [ 25 ]	16.08	0.581	0.632	18.83	0.435	0.421	19.96	0.669	0.194	20.07	0.632	0.379	18.73	0.579	0.406
RobustSplat [ 12 ]	19.17	0.622	0.326	22.40	0.693	0.270	20.76	0.797	0.200	22.86	0.795	0.193	21.29	0.726	0.247
WeatherGS [ 39 ]	17.50	0.547	0.383	21.04	0.604	0.270	19.89	0.601	0.331	21.16	0.677	0.317	19.89	0.607	0.325
RainyScape [ 34 ]	19.33	0.632	0.270	23.83	0.714	0.212	22.64	0.832	0.137	23.57	0.792	0.156	22.34	0.742	0.193
Ours	20.31	0.671	0.249	24.21	0.739	0.187	24.06	0.867	0.103	24.52	0.841	0.112	23.27	0.779	0.162

Table 3: Quantitative comparisons on snowy scenes. The best and second-best scores are color-encoded for clarity.

Panel of Figure 5 — Figure 5: Qualitative results on snowy scenes. Best viewed zoomed in.

Method	Bicycle			Garden			Factory			Tanabata			Average
Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
3DGS [ 20 ]	15.71	0.473	0.370	15.86	0.588	0.327	15.28	0.681	0.240	14.30	0.500	0.392	15.29	0.561	0.332
RobustSplat [ 12 ]	15.93	0.474	0.375	15.98	0.579	0.338	15.40	0.688	0.230	14.98	0.531	0.365	15.57	0.568	0.327
WeatherGS [ 39 ]	15.96	0.489	0.367	15.65	0.574	0.469	15.75	0.509	0.416	14.88	0.531	0.430	15.56	0.526	0.420
OR [ 15 ] +3DGS [ 20 ]	17.12	0.529	0.366	20.18	0.618	0.324	23.53	0.836	0.177	19.51	0.659	0.299	20.09	0.661	0.292
Ours	19.74	0.603	0.327	22.12	0.708	0.209	24.02	0.880	0.099	23.12	0.777	0.173	22.25	0.742	0.202

Table 4: Quantitative comparisons on hybrid-weather scenes (haze, rain, and snow). The best and second-best scores are color-encoded.

Panel of Figure 6 — Figure 6: Qualitative results on hybrid-weather scenes (haze, rain, and snow). Best viewed zoomed in.

Variant	Type	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
Gaussian-Driven Extinction	Hazy	19.29	0.709	0.284
Uniform Extinction		20.55	0.749	0.211
Ours		20.79	0.776	0.190
Restored-Derived Residuals	Rainy	29.65	0.844	0.227
Ours	Rainy	32.41	0.904	0.139
w/o GGS	Hybrid	21.86	0.721	0.237

Table 5: Ablation study of CSM (top block), PLM (middle block), and GGS (bottom block) under hazy, rainy, and hybrid-weather settings. The best results are marked in bold .

scene images

\{I_{\text{in}}\}

, initialized 3D Gaussians

\mathcal{G}

sampling count

K

, reference radius

r_{0}

loss weights

\lambda_{r}

, iteration numbers

M_{\text{init}}

(geometry initialization) and

M_{\text{joint}}

(joint

optimization), residual refresh interval

Z_{\text{ref}}

Algorithm 1 Training of NimbusGS

Figure 7: Qualitative ablation of Particulate Layer Modeling. Best viewed zoomed in.

Figure 8: Qualitative ablation of Continuous Scattering Modeling.

Figure 9: Qualitative ablation of Geometry-Guided Gradient Scaling. Best viewed zoomed in.

Variant	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
w/o Depth	21.98	0.731	0.214
w/o Projected Radius	22.14	0.733	0.209
w/o Reconstruction Error	22.09	0.729	0.211
Ours	22.25	0.742	0.202

Table 6: Factor-wise ablation of GGS. Best results are marked in bold .

Variant	PSNR↑	SSIM↑	LPIPS↓
w/o $\mathcal{L}_{\text{DCP}}$	17.32	0.610	0.233
w/ $\mathcal{L}_{\text{DCP}}$ (stage 2)	20.92	0.718	0.220
w/o $\mathcal{L}_{\text{TV}}$	22.17	0.735	0.211
Ours	22.25	0.742	0.202

Table 7: Ablation of loss terms. Best results are marked in bold .

$M_{\text{init}}$	Bicycle			Garden
$M_{\text{init}}$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
1K	17.69	0.521	0.408	20.64	0.651	0.280
2K	18.01	0.573	0.345	21.75	0.679	0.256
4K	19.74	0.603	0.327	22.12	0.708	0.209
10K	19.16	0.593	0.335	22.09	0.703	0.237
15K	19.24	0.591	0.334	22.07	0.702	0.241

Table 8: Ablation of geometry-initialization iteration count. Best results are marked in bold .

Method	Params.	Memory	Training	FPS
3DGS [ 20 ]	94.1M	5.3G	29min	171
SeaSplat [ 49 ]	29.4M	5.1G	126min	169
WaterSplatting [ 24 ]	38.9M	5.4G	19min	32
RainyScape [ 34 ]	86.8M	8.3G	169min	167
RobustSplat [ 12 ]	81.0M	8.9G	29min	168
WeatherGS [ 39 ]	108.0M	19.9G	28min	159

Table 9: Model complexity and runtime comparison under the H+R+S setting. Params., Memory, Training, and FPS denote parameter count, training memory usage, training time, and rendering speed, respectively.

Method	H+R
	Stump			Wine
	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
3DGS [ 20 ]	17.31	0.572	0.335	15.09	0.551	0.368
SeaSplat [ 49 ]	16.36	0.533	0.368	12.77	0.531	0.367
WaterSplatting [ 24 ]	17.49	0.538	0.340	14.45	0.518	0.305
DerainNeRF [ 25 ]	17.39	0.569	0.332	15.10	0.543	0.387

Table 10: Quantitative comparisons on the additional Stump and Wine scenes. The three blocks correspond to the H+R, H+S, and R+S settings. The best and second-best scores are color-encoded for clarity.

Method	Bicycle			Garden			Factory			Tanabata			Average
Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
3DGS [ 20 ]	15.24	0.491	0.370	15.95	0.530	0.376	13.90	0.534	0.406	13.58	0.445	0.428	14.66	0.500	0.395
SeaSplat [ 49 ]	15.48	0.523	0.354	13.56	0.558	0.359	14.64	0.621	0.329	14.58	0.564	0.324	14.56	0.566	0.341
WaterSplatting [ 24 ]	16.69	0.537	0.360	17.42	0.592	0.312	14.71	0.602	0.307	14.45	0.518	0.309	15.81	0.562	0.322
DerainNeRF [ 25 ]	16.10	0.499	0.401	16.27	0.556	0.350	14.29	0.583	0.372	14.31	0.503	0.407	15.24	0.535	0.382
RainyScape [ 34 ]	16.66	0.504	0.387	18.22	0.653	0.267	14.75	0.642	0.268	14.65	0.527	0.306	16.07	0.581	0.307

Table 11: Quantitative comparisons on H+R scenes. The best and second-best scores are color-encoded for clarity.

Method	Bicycle			Garden			Factory			Tanabata			Average
Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
3DGS [ 20 ]	17.19	0.554	0.327	14.21	0.507	0.411	13.87	0.601	0.282	15.16	0.615	0.261	15.10	0.569	0.320
SeaSplat [ 49 ]	15.73	0.525	0.348	14.66	0.513	0.400	13.98	0.626	0.247	15.18	0.625	0.339	14.88	0.572	0.333
WaterSplatting [ 24 ]	16.47	0.530	0.325	14.10	0.512	0.422	14.42	0.613	0.276	15.37	0.623	0.251	15.09	0.569	0.318
DerainNeRF [ 25 ]	17.12	0.558	0.331	14.32	0.576	0.378	14.12	0.611	0.279	15.16	0.617	0.288	15.18	0.590	0.319
RainyScape [ 34 ]	17.33	0.567	0.256	14.52	0.600	0.369	14.08	0.631	0.241	15.75	0.641	0.211	15.42	0.609	0.269

Table 12: Quantitative comparisons on H+S scenes. The best and second-best scores are color-encoded for clarity.

Method	Bicycle			Garden			Factory			Tanabata			Average
Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
3DGS [ 20 ]	19.25	0.590	0.332	23.39	0.714	0.245	18.29	0.685	0.306	22.79	0.765	0.241	20.93	0.688	0.281
DerainNeRF [ 25 ]	18.37	0.533	0.369	23.12	0.712	0.295	18.62	0.675	0.297	22.89	0.762	0.255	20.75	0.670	0.304
RainyScape [ 34 ]	19.57	0.614	0.285	24.25	0.729	0.216	22.12	0.805	0.127	24.07	0.808	0.117	22.50	0.739	0.186
RobustSplat [ 12 ]	19.56	0.588	0.348	23.70	0.718	0.271	18.99	0.701	0.295	22.86	0.785	0.244	21.27	0.698	0.289
WeatherGS [ 39 ]	17.47	0.539	0.413	20.71	0.618	0.442	19.11	0.562	0.364	22.01	0.733	0.243	19.82	0.613	0.365

Table 13: Quantitative comparisons on R+S scenes. The best and second-best scores are color-encoded for clarity.

Figure 10: Qualitative comparisons on single-weather scenes (haze, rain, snow).

Figure 11: Qualitative comparisons on hybrid-weather scenes (H+R, H+S, R+S, H+R+S). Best viewed zoomed in.

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Although image deraining has advanced substantially, existing methods mainly focus on 2D image restoration. As spatial intelligence applications such as embodied AI and autonomous driving continue to emerge, reconstructing clean 3D scenes from sparse rainy views in a feed-forward manner becomes increasingly important. Existing feed-forward 3D Gaussian Splatting (3DGS) methods often assume clean inputs and collapse under rainy conditions. To this end, we present DerainSplat, a feed-forward framework that reconstructs clean 3D scenes from only a few rainy views. To support this task, we build a large-scale multi-view derain dataset through a four-stage synthesis pipeline that sequentially models overcast illumination, depth-dependent haze, rain streaks, and lens raindrops, producing privileged weather factors. We introduce a weather net that predicts the weather factors from rainy context and yields two support maps. Scene support modulates cross-view cost-volume matching, while radiance support drives depth-aligned appearance fusion to fill corrupted pixels. The derived geometry evidence further attenuates Gaussian opacity to reduce spurious structures. A rainy cycle consistency re-renders clean views using the predicted factors and aligns them with rainy inputs. Extensive experiments show that DerainSplat outperforms existing methods on various datasets, including RealEstate10K, ACID, Mip-NeRF360, and real-world rainy scenes, with strong cross-dataset generalization.

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