In this work, we present WeatherEdit, a novel weather editing pipeline for generating realistic weather effects with controllable types and severity in 3D scenes. Our approach is structured into two key components: weather background editing and weather particle construction. For weather background editing, we introduce an all-in-one adapter that integrates multiple weather styles into a single diffusion model, enabling the generation of diverse weather effects in 2D image backgrounds. During inference, we design a Temporal-View (TV-) attention mechanism that follows a specific order to aggregate temporal and spatial information, ensuring consistent editing across multi-frame and multi-view images. To construct the weather particles, we first reconstruct a 3D scene using the edited images and then introduce a 4D Gaussian field to generate snowflakes, raindrops and fog in the scene. The attributes and dynamics of these particles are controlled through attribute modelling and dynamic simulation, ensuring realistic weather representation and flexible severity adjustments. Finally, we integrate the 4D Gaussian field with the 3D scene to render consistent and highly realistic weather effects. Experiments on multiple driving datasets demonstrate that WeatherEdit can generate diverse weather effects with controllable condition severity, highlighting its potential for autonomous driving simulation in adverse weather.

In this work, we present GaussianSlicer, an efficient plane-based Gaussian Splatting framework for reconstructing geometry from cross-sectional slices input. Unlike previous methods that rely on computationally intensive geometry-based or grid-based implicit techniques, which struggle with complex cases (e.g., sparse slices, multi-hole geometries). GaussianSlicer enables parallel optimization without requiring any prior constraints. Our method begins by initializing planar Gaussians on each slice and optimizing their layout to obtain accurate geometry representation. To align the Gaussian splats, we introduce a geometric regularization that promotes surface smoothness and ensures consistency in global topology. Our system enables accurate 3D reconstruction from sparse, irregular, multi-label slices with high computational efficiency. Experimental results show that, on average, our method is 45.62% faster and achieves a 21.91% improvement in Chamfer Distance (CD) outperforming state-of-the-art methods on the collected dataset.

D 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 3DGSbased framework for reconstructing clear scenes from multiview 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. See project: https://jumponthemoon.github.io/weather-gs.