Abstract
Polarization images facilitate image enhancement and 3D reconstruction tasks, but the limited accessibility of polarization cameras hinders their broader application. This gap drives the need for synthesizing photorealistic polarization this http URL existing polarization simulator Mitsuba relies on a parametric polarization image formation model and requires extensive 3D assets covering shape and PBR materials, preventing it from generating large-scale photorealistic images. To address this problem, we propose PolarAnything, capable of synthesizing polarization images from a single RGB input with both photorealism and physical accuracy, eliminating the dependency on 3D asset collections. Drawing inspiration from the zero-shot performance of pretrained diffusion models, we introduce a diffusion-based generative framework with an effective representation strategy that preserves the fidelity of polarization properties. Experiments show that our model generates high-quality polarization images and supports downstream tasks like shape from polarization.
Abstract (translated)
偏振图像促进了图像增强和三维重建等任务，但极化相机的有限可访问性阻碍了它们更广泛的应用。这种差距推动了合成逼真的极化图像的需求。现有的极化模拟器Mitsuba依赖于一个参数化的极化图像生成模型，并需要大量的3D资产（包括形状和物理基础渲染材料），这使其无法生成大规模的逼真图像。为了解决这个问题，我们提出了PolarAnything，它可以从单个RGB输入合成具有逼真度和物理准确性的偏振图像，从而消除对3D资源集的需求。 受到预训练扩散模型零样本性能的启发，我们引入了一个基于扩散的生成框架，并采用了一种有效的表示策略来保持极化属性的保真度。实验表明，我们的模型可以生成高质量的偏振图像并支持诸如从偏振中获取形状等下游任务。
URL
https://arxiv.org/abs/2507.17268
