DerainSplat: Feed-Forward Clean 3D Gaussian Splatting from Sparse Rainy Views

• URL: http://arxiv.org/abs/2608.02191v1
• Date: Mon, 03 Aug 2026 13:15:37 GMT
• Title: DerainSplat: Feed-Forward Clean 3D Gaussian Splatting from Sparse Rainy Views
• Abstract summary: textbftextitDerainSplat reconstructs clean 3D scenes from only a few rainy views.<n>textbftextitDerainSplat outperforms existing methods on various datasets.
• Score: 7.477699693479806
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 \textbf{\textit{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 \textbf{\textit{DerainSplat}} outperforms existing methods on various datasets, including RealEstate10K, ACID, Mip-NeRF360, and real-world rainy scenes, with strong cross-dataset generalization.

Related papers

• Rethinking Rainy 3D Scene Reconstruction via Perspective Transforming and Brightness Tuning [8.552623283033695]
  Existing datasets often overlook two critical characteristics of real rainy 3D scenes.<n>We construct a new dataset named OmniRain3D that incorporates perspective heterogeneity and brightness dynamicity.<n>We propose an end-to-end reconstruction framework named REVR-GSNet, achieving high-fidelity reconstruction of clean 3D scenes from rain-degraded inputs.
  arXiv  Detail & Related papers  (2025-11-10T05:57:55Z)
• Raindrop GS: A Benchmark for 3D Gaussian Splatting under Raindrop Conditions [23.642500549050027]
  RaindropGS is a benchmark designed to evaluate the full 3DGS pipeline-from unconstrained, raindrop-corrupted images to clear 3DGS reconstructions.<n>First, we collect a real-world raindrop reconstruction dataset, in which each scene contains three aligned image sets.<n>Through comprehensive experiments and analyses, we reveal critical insights into the performance limitations of existing 3DGS methods on unconstrained raindrop images.
  arXiv  Detail & Related papers  (2025-10-20T16:36:15Z)
• Semi-Supervised State-Space Model with Dynamic Stacking Filter for Real-World Video Deraining [73.5575992346396]
  We propose a dual-branch-temporal state-space model to enhance rain streak removal in video sequences.<n>To improve multi-frame feature fusion, we derive a dynamic filter stacking, which adaptively approximates statistical filters for pixel-wise feature refinement.<n>To further explore the capacity of deraining models in supporting other vision-based tasks in rainy environments, we introduce a novel real-world benchmark.
  arXiv  Detail & Related papers  (2025-05-22T15:50:00Z)
• SpikeDerain: Unveiling Clear Videos from Rainy Sequences Using Color Spike Streams [49.34425133546994]
  Restoring clear frames from rainy videos presents a significant challenge due to the rapid motion of rain streaks.<n>Traditional frame-based visual sensors, which capture scene content synchronously, struggle to capture the fast-moving details of rain accurately.<n>We propose a Color Spike Stream Deraining Network (SpikeDerain), capable of reconstructing spike streams of dynamic scenes and accurately removing rain streaks.
  arXiv  Detail & Related papers  (2025-03-26T08:28:28Z)
• DeRainGS: Gaussian Splatting for Enhanced Scene Reconstruction in Rainy Environments [4.86090922870914]
  This study introduces the novel task of 3D Reconstruction in Rainy Environments (3DRRE) To benchmark this task, we construct the HydroViews dataset that comprises a diverse collection of both synthesized and real-world scene images. We propose DeRainGS, the first 3DGS method tailored for reconstruction in adverse rainy environments.
  arXiv  Detail & Related papers  (2024-08-21T11:39:18Z)
• RainyScape: Unsupervised Rainy Scene Reconstruction using Decoupled Neural Rendering [50.14860376758962]
  We propose RainyScape, an unsupervised framework for reconstructing clean scenes from a collection of multi-view rainy images. Based on the spectral bias property of neural networks, we first optimize the neural rendering pipeline to obtain a low-frequency scene representation. We jointly optimize the two modules, driven by the proposed adaptive direction-sensitive gradient-based reconstruction loss.
  arXiv  Detail & Related papers  (2024-04-17T14:07:22Z)
• Dual Degradation Representation for Joint Deraining and Low-Light Enhancement in the Dark [57.85378202032541]
  Rain in the dark poses a significant challenge to deploying real-world applications such as autonomous driving, surveillance systems, and night photography. Existing low-light enhancement or deraining methods struggle to brighten low-light conditions and remove rain simultaneously. We introduce an end-to-end model called L$2$RIRNet, designed to manage both low-light enhancement and deraining in real-world settings.
  arXiv  Detail & Related papers  (2023-05-06T10:17:42Z)
• Semi-MoreGAN: A New Semi-supervised Generative Adversarial Network for Mixture of Rain Removal [18.04268933542476]
  We propose a new SEMI-supervised Mixture Of rain REmoval Generative Adversarial Network (Semi-MoreGAN) Semi-MoreGAN consists of four key modules: (I) a novel attentional depth prediction network to provide precise depth estimation; (ii) a context feature prediction network composed of several well-designed detailed residual blocks to produce detailed image context features; (iii) a pyramid depth-guided non-local network to effectively integrate the image context with the depth information, and produce the final rain-free images; and (iv) a comprehensive semi-supervised loss function to make the model not limited
  arXiv  Detail & Related papers  (2022-04-28T11:35:26Z)
• Semi-DRDNet Semi-supervised Detail-recovery Image Deraining Network via Unpaired Contrastive Learning [59.22620253308322]
  We propose a semi-supervised detail-recovery image deraining network (termed as Semi-DRDNet) As a semi-supervised learning paradigm, Semi-DRDNet operates smoothly on both synthetic and real-world rainy data in terms of deraining robustness and detail accuracy.
  arXiv  Detail & Related papers  (2022-04-06T12:35:27Z)
• RCDNet: An Interpretable Rain Convolutional Dictionary Network for Single Image Deraining [49.99207211126791]
  We specifically build a novel deep architecture, called rain convolutional dictionary network (RCDNet) RCDNet embeds the intrinsic priors of rain streaks and has clear interpretability. By end-to-end training such an interpretable network, all involved rain kernels and proximal operators can be automatically extracted.
  arXiv  Detail & Related papers  (2021-07-14T16:08:11Z)
• Multi-Scale Progressive Fusion Network for Single Image Deraining [84.0466298828417]
  Rain streaks in the air appear in various blurring degrees and resolutions due to different distances from their positions to the camera. Similar rain patterns are visible in a rain image as well as its multi-scale (or multi-resolution) versions. In this work, we explore the multi-scale collaborative representation for rain streaks from the perspective of input image scales and hierarchical deep features.
  arXiv  Detail & Related papers  (2020-03-24T17:22:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.