RainyScape: Unsupervised Rainy Scene Reconstruction using Decoupled Neural Rendering

• URL: http://arxiv.org/abs/2404.11401v1
• Date: Wed, 17 Apr 2024 14:07:22 GMT
• Title: RainyScape: Unsupervised Rainy Scene Reconstruction using Decoupled Neural Rendering
• Authors: Xianqiang Lyu, Hui Liu, Junhui Hou,
• Abstract summary: We propose RainyScape, an unsupervised framework for reconstructing clean scenes from a collection of multi-view rainy images. Based on the spectral bias property of neural networks, we first optimize the neural rendering pipeline to obtain a low-frequency scene representation. We jointly optimize the two modules, driven by the proposed adaptive direction-sensitive gradient-based reconstruction loss.
• Score: 50.14860376758962
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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