AI 中文总结
DerainSplat是一种前馈框架，可从少量雨天视图重建干净3D场景，通过四阶段合成数据集与天气网络等设计，在多类数据集上优于现有方法且泛化性强。
AI 中文摘要
尽管图像去雨技术已取得显著进展，现有方法主要聚焦于2D图像恢复。随着具身AI、自动驾驶等空间智能应用不断兴起，以前馈方式从稀疏雨天视图重建干净3D场景变得愈发重要。现有前馈式3D高斯溅射（3DGS）方法通常假设输入为干净图像，在雨天条件下会失效。为此，我们提出DerainSplat，这是一种仅用少量雨天视图重建干净3D场景的前馈框架。为支撑该任务，我们通过四阶段合成流水线构建了大规模多视图去雨数据集，该流水线依次模拟阴天光照、深度相关雾霾、雨条纹及镜头雨滴，生成特权天气因子。我们引入天气网络，从雨天上下文预测天气因子并生成两张支持图：场景支持图调节跨视图代价体积匹配，辐射支持图驱动深度对齐的外观融合以填充损坏像素；推导的几何证据进一步降低高斯不透明度以减少虚假结构；雨天循环一致性利用预测因子重新渲染干净视图，并与雨天输入对齐。大量实验表明，DerainSplat在RealEstate10K、ACID、Mip-NeRF360及真实雨天场景等各类数据集上均优于现有方法，具备较强的跨数据集泛化能力。
英文摘要
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.
