RainyScape: Unsupervised Rainy Scene Reconstruction using Decoupled Neural Rendering

Xianqiang Lyu, Hui LIU, Junhui Hou

Abstract: We propose RainyScape, an unsupervised framework for reconstructing clean scenes from a collection of multi-view rainy images. RainyScape consists of two main modules: a neural rendering module and a rain-prediction module that incorporates a predictor network and a learnable latent embedding that captures the rain characteristics of the scene. Specifically, based on the spectral bias property of neural networks, we first optimize the neural rendering pipeline to obtain a low-frequency scene representation. Subsequently, we jointly optimize the two modules, driven by the proposed adaptive direction-sensitive gradient-based reconstruction loss, which encourages the network to distinguish between scene details and rain streaks, facilitating the propagation of gradients to the relevant components. Extensive experiments on both the classic neural radiance field and the recently proposed 3D Gaussian splatting demonstrate the superiority of our method in effectively eliminating rain streaks and rendering clean images, achieving state-of-the-art performance. The constructed high-quality dataset and source code will be publicly available.

Primary Subject Area: [Generation] Generative Multimedia

Secondary Subject Area: [Content] Media Interpretation

Relevance To Conference: The proposed method can generate from a set of degraded multiview images by rain clean novel views, which will advance the field of content generation.

Submission Number: 3249