3D Gaussian Splatting (3DGS) has gained significant attention for 3D scene reconstruction, but still suffers from complex outdoor environments, especially under adverse weather. This is because 3DGS treats the artifacts caused by adverse weather as part of the scene and will directly reconstruct them, largely reducing the clarity of the reconstructed scene. To address this challenge, we propose WeatherGS, a 3DGS-based framework for reconstructing clear scenes from multi-view images under different weather conditions. Specifically, we explicitly categorize the multi-weather artifacts into the dense particles and lens occlusions that have very different characters, in which the former are caused by snowflakes and raindrops in the air, and the latter are raised by the precipitation on the camera lens. In light of this, we propose a dense-to-sparse preprocess strategy, which sequentially removes the dense particles by an Atmospheric Effect Filter (AEF) and then extracts the relatively sparse occlusion masks with a Lens Effect Detector (LED). Finally, we train a set of 3D Gaussians by the processed images and generated masks for excluding occluded areas, and accurately recover the underlying clear scene by Gaussian splatting. We conduct a diverse and challenging benchmark to facilitate the evaluation of 3D reconstruction under complex weather scenarios. Extensive experiments on this benchmark demonstrate that our WeatherGS consistently produces high-quality, clean scenes across various weather scenarios, outperforming existing state-of-the-art methods.
三维高斯散射（3D Gaussian Splatting，3DGS）在三维场景重建领域备受关注，但在复杂的户外环境中，尤其是在恶劣天气条件下仍存在挑战。这是因为3DGS将恶劣天气引起的伪影视为场景的一部分，直接对其进行重建，显著降低了重建场景的清晰度。为了解决这一问题，我们提出了WeatherGS，一种基于3DGS的框架，用于在不同天气条件下从多视角图像中重建清晰的场景。 具体而言，我们明确将多种天气伪影分为两类：密集颗粒和镜头遮挡，这两者具有截然不同的特性。其中，密集颗粒由空气中的雪花和雨滴引起，而镜头遮挡则是由于降水附着在相机镜头上造成的。基于这一分类，我们提出了一种“密到疏”的预处理策略，依次使用大气效应滤波器（Atmospheric Effect Filter, AEF）去除密集颗粒，然后利用镜头效应检测器（Lens Effect Detector, LED）提取相对稀疏的遮挡掩膜。最后，我们通过处理后的图像和生成的遮挡掩膜训练一组三维高斯，并排除遮挡区域，通过高斯散射精确恢复场景的清晰内容。 我们构建了一个多样化且具有挑战性的基准，用于评估复杂天气场景下的三维重建性能。大量实验表明，在该基准上，WeatherGS在各种天气条件下始终生成高质量、干净的场景，其性能显著优于现有的最先进方法。