WeatherGS: 3D Scene Reconstruction in Adverse Weather Conditions via Gaussian Splatting

Abstract

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 denseto-sparse preprocess strategy, which sequentially removes the dense particles by an Atmospheric Effect Filter (AEF) and then extracts the relatively sparse occlusio