OmniRaindrop: A Comprehensive Benchmark for Dual-Focused Raindrop Removal under Day and Night Scenes

Zhiqiang Teng; Tingting Chen; Beibei Lin; Zifeng Yuan; Xuanyi Li; Xuanyu Zhang; Shunli Zhang

cs.CVOct 20, 2025

OmniRaindrop: A Comprehensive Benchmark for Dual-Focused Raindrop Removal under Day and Night Scenes

Authors: Zhiqiang Teng, Tingting Chen, Beibei Lin, Zifeng Yuan, Xuanyi Li, Xuanyu Zhang, Shunli Zhang

Organizations: Beijing Jiaotong University · National University of Singapore

Abstract

Adherent raindrops on camera lenses are a pervasive source of real-world image degradation, undermining outdoor visual perception for downstream applications such as robotics and 3D scene reconstruction. However, most existing raindrop datasets and methods target only background-focused 2D images, overlooking the equally common raindrop-focused case and the impact of raindrops on 3D scene reconstruction. Moreover, they are almost exclusively daytime, lacking the complex artificial lighting and reflections of nighttime raindrop scenes. To address these issues, we introduce OmniRaindrop, a real-world dataset and benchmark that jointly evaluates 2D raindrop removal and 3D scene reconstruction across raindrop-focused and background-focused captures, under both daytime and nighttime conditions. OmniRaindrop provides spatially aligned raindrop-focused, background-focused, and rain-free ground-truth image sets: a 2D subset of 15,186 aligned pairs/triplets and a 3D subset of 40 real-world scenes evenly split between day and night, totaling 3,822 multi-view images. On top of this data, the benchmark traces how raindrop degradation propagates from 2D restoration to 3D reconstruction by testing representative restoration methods and their effects on pose estimation, point cloud initialization, and 3DGS optimization. Comprehensive experiments reveal that raindrop-focused scenes are substantially harder than background-focused ones, and that errors in 2D restoration and pose/point cloud initialization cascade into degraded 3D reconstruction, pointing to clear directions for more robust 2D restoration and 3DGS design. Building on the dataset, we have organized two consecutive NTIRE challenges (2025 and 2026) on Day and Night Raindrop Removal for Dual-Focused Images.

Figures & tables

Figure 1: Motivation for OmniRaindrop. Top block: background-focused captures (blurry raindrops) Qian et al. (2018) ; Soboleva and Shipitko (2021) ; Jin et al. (2024a) ; bottom block: raindrop-focused captures (blurry background); within each block, the upper and lower rows show daytime and nighttime, respectively Jin et al. (2024a) .

Figure 2: Illustration of the OmniRaindrop dataset. OmniRaindrop covers day/night and raindrop-/background-focused settings, providing aligned 2D pairs/triplets and real 3D multi-view scenes with rain-free references for evaluating 2D raindrop removal and 3DGS reconstruction.

Figure 3: Physical formulation of raindrop-focused degradation. Given a raindrop-focused image $\tilde{x}$ and its corresponding rain-free blurred background $x_{0}$ , the difference map $\tilde{m}$ is computed to isolate raindrop-induced degradation, while $b_{0}$ denotes the sharp rain-free ground truth.

Figure 4: From 2D to 3D raindrop removal benchmark pipeline. We develop the first benchmark for comprehensively evaluating raindrop removal performance. The benchmark begins with dataset collection, proceeds through data processing, and ends with a raindrop-aware 3DGS evaluation. Our benchmark enables the community to explore background-focused and raindrop-focused images, including challenges unique to daytime and nighttime conditions.

Dataset	Components	Scale		Source	Bg-focus		Rd-focus
Dataset	Components	Scenes	Images	Source	Day	Night	Day	Night
2D Raindrop Datasets
Qian et al. Qian et al. (2018)	919 Pairs	$-$	1,838	Real	✓	$\times$	$\times$	$\times$
RainDS-Real Quan et al. (2021)	248 Quadruplets	$-$	992	Real	✓	$\times$	$\times$	$\times$
RainDS-Syn Quan et al. (2021)	1,200 Quadruplets	$-$	4,800	Syn	✓	$\times$	$\times$	$\times$
RobotCar Porav et al. (2019)	4,818 Pairs	$-$	9,636	Real	✓	$\times$	$\times$	$\times$

Table 1: Comprehensive comparison between OmniRaindrop and existing datasets. We compare dataset components, scale, data source, focus condition, and illumination coverage. OmniRaindrop provides a large-scale real-world benchmark with strict alignment for both 2D restoration and 3DGS reconstruction.

Data/Metric		Input	Raindrop Algorithms					Restoration Backbones
Data/Metric		Input	AtGAN	Robot	RainDS	IDT	RDiff	Restormer	Uformer	DiT
Day	PSNR $\uparrow$	21.92	23.62	23.82	25.39	26.05	25.52	26.08	25.78	26.03
	SSIM $\uparrow$	0.560	0.658	0.670	0.717	0.736	0.734	0.748	0.727	0.752
	LPIPS $\downarrow$	0.247	0.200	0.191	0.147	0.141	0.111	0.131	0.148	0.106
Night	PSNR $\uparrow$	24.78	24.38	24.57	25.39	26.81	26.48	26.87	25.26	26.23
	SSIM $\uparrow$	0.726	0.773	0.787	0.820	0.851	0.831	0.848	0.816	0.826

Table 2: Quantitative evaluation of representative raindrop removal methods and general image restoration backbones on the daytime and nighttime subsets of OmniRaindrop. We report PSNR, SSIM, and LPIPS to evaluate pixel fidelity, structural similarity, and perceptual quality, respectively. The best results are highlighted for each metric.

Figure 5: Qualitative comparison of representative raindrop removal methods on daytime images. Existing methods remove some diffuse occlusions in background-focused inputs, but struggle with the sharper raindrops and severe background blur in raindrop-focused inputs.

Figure 6: Qualitative comparison of representative raindrop removal methods on nighttime images. Compared with daytime scenes, nighttime raindrops introduce stronger light refraction, specular highlights, halos, local saturation, and non-linear intensity variations. Existing methods often leave residual raindrops or over-smooth background details in background-focused images. Under raindrop-focused inputs, the combined effect of sharp raindrop structures, severe background defocus, and nighttime illumination distortion makes it more difficult to recover clean and structurally faithful backgrounds.

		3DGS			3DGS-MCMC			GS-W			WeatherGS
Scene	Metrics	Uformer	Restormer	IDT	Uformer	Restormer	IDT	Uformer	Restormer	IDT	/
Day	PSNR $\uparrow$	17.119	17.162	17.211	18.202	18.217	18.217	19.005	18.983	19.192	16.918
	SSIM $\uparrow$	0.468	0.470	0.476	0.501	0.494	0.502	0.540	0.538	0.550	0.443
	LPIPS $\downarrow$	0.442	0.439	0.434	0.419	0.419	0.414	0.355	0.355	0.340	0.446
Night	PSNR $\uparrow$	20.633	20.744	20.862	21.222	21.476	21.537	22.254	22.268	22.162	20.672
	SSIM $\uparrow$	0.536	0.540	0.544	0.622	0.620	0.622	0.596	0.588	0.580	0.518

Table 3: Quantitative comparison of four 3DGS methods on the 3D subset using VGGT Wang et al. (2025a) for camera pose estimation and point cloud initialization. For each daytime/nighttime split, results are averaged over all background-focused and raindrop-focused cases. Uformer Wang et al. (2022) , Restormer Zamir et al. (2022) , and IDT Xiao et al. (2022) denote different 2D restoration front-ends used before 3DGS reconstruction, while WeatherGS Qian et al. (2025) is evaluated with its original pipeline.

Figure 7: Qualitative comparison of four 3DGS methods under raindrop-corrupted inputs. Background-focused inputs generally preserve more scene details and lead to higher reconstruction quality than raindrop-focused inputs. WeatherGS Qian et al. (2025) produces relatively smooth reconstructions for blurred inputs, while GS-W Zhang et al. (2024) produces the most visually coherent results in these examples.

Figure 8: Qualitative comparison of four 3DGS methods under nighttime raindrop-corrupted inputs. Nighttime scenes introduce complex artificial-light refraction, highlights, and low-light degradation, which can introduce inconsistencies across views. Compared with background-focused inputs, raindrop-focused inputs lead to more severe structural degradation due to sharp raindrop interference and defocused background observations.

Method	background-		raindrop-
	focus		focus
	Empty Scene	Avg. Points	Empty Scene	Avg. Points
COLMAP	0/40	6,199.55	1/40	1,634.54
VGGT	9/40	82,069.97	37/40	6,402.00

Table 4: Comparison of point cloud initialization generated by COLMAP Schonberger and Frahm (2016) and VGGT Wang et al. (2025a) under background-focus and raindrop-focus inputs. Empty scenes denote completed runs that produce zero point or invalid initial point cloud files. Average point counts are computed only over scenes with valid point clouds.

Figure 9: Camera pose quality comparison between COLMAP Schonberger and Frahm (2016) and VGGT Wang et al. (2025a) under background-focused and raindrop-focused inputs. We report four metrics across representative daytime and nighttime scenes: (a) registration rate, (b) rotation error, (c) translation error, and (d) AUC@30. Raindrop-focused inputs substantially reduce registration stability and pose accuracy, especially for COLMAP, while VGGT shows stronger robustness but still degrades under severe raindrop-focused conditions.

Input Image	Geometry Initialization	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
Raindrop	Raindrop	19.037	0.510	0.415
Raindrop	GT-image	21.310	0.620	0.363
GT	Raindrop	20.464	0.551	0.333
GT	GT-image	25.525	0.811	0.156

Table 5: Ablation study of appearance inputs and geometry initialization for 3DGS reconstruction. In the Input Image column, “Raindrop” denotes raindrop-corrupted images restored by IDT Xiao et al. (2022) before being used for 3DGS optimization, whereas “GT” denotes the corresponding rain-free ground-truth images. In the Geometry Initialization column, “Raindrop” and “GT” indicate that camera poses and initial point clouds are estimated from the original, unprocessed raindrop-corrupted images and the rain-free ground-truth images, respectively.

3D Reconstruction

3D reconstruction recovers a scene’s shape, appearance, and camera motion from images, video, depth, or other sensor measurements.

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NTIRE 2026 The Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results

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Open paper AskPDF arXiv

This paper presents an overview of the NTIRE 2026 Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images. Building upon the success of the first edition, this challenge attracted a wide range of impressive solutions, all developed and evaluated on our real-world Raindrop Clarity dataset~\cite{jin2024raindrop}. For this edition, we adjust the dataset with 14,139 images for training, 407 images for validation, and 593 images for testing. The primary goal of this challenge is to establish a strong and practical benchmark for the removal of raindrops under various illumination and focus conditions. In total, 168 teams have registered for the competition, and 17 teams submitted valid final solutions and fact sheets for the testing phase. The submitted methods achieved strong performance on the Raindrop Clarity dataset, demonstrating the growing progress in this challenging task.

Abstract

3D Reconstruction

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