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首页> 外文会议>International Conference on 3D Vision >Intrinsic Autoencoders for Joint Deferred Neural Rendering and Intrinsic Image Decomposition
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Intrinsic Autoencoders for Joint Deferred Neural Rendering and Intrinsic Image Decomposition

机译：用于关节延迟神经渲染和内在图像分解的内在自动化器

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Neural rendering techniques promise efficient photorealistic image synthesis while providing rich control over scene parameters by learning the physical image formation process. While several supervised methods have been proposed for this task, acquiring a dataset of images with accurately aligned 3D models is very difficult. The main contribution of this work is to lift this restriction by training a neural rendering algorithm from unpaired data. We propose an autoencoder for joint generation of realistic images from synthetic 3D models while simultaneously decomposing real images into their intrinsic shape and appearance properties. In contrast to a traditional graphics pipeline, our approach does not require to specify all scene properties, such as material parameters and lighting by hand. Instead, we learn photo-realistic deferred rendering from a small set of 3D models and a larger set of unaligned real images, both of which are easy to acquire in practice. Simultaneously, we obtain accurate intrinsic decompositions of real images while not requiring paired ground truth. Our experiments confirm that a joint treatment of rendering and decomposition is indeed beneficial and that our approach outperforms state-of-the-art image-to-image translation baselines both qualitatively and quantitatively.
机译：神经渲染技术承诺通过学习物理图像形成过程提供高效的光电型图像综合，同时提供丰富的控制场景参数。虽然已经提出了几种监督方法为此任务，获取具有精确对齐的3D模型的图像数据集非常困难。这项工作的主要贡献是通过从未配对数据训练神经渲染算法来提升这种限制。我们提出了一种自动阳极，用于从合成3D模型中联合生成现实图像，同时将真实图像与其固有形状和外观特性分解。与传统的图形管道相比，我们的方法不需要指定所有场景属性，例如材料参数和手动点亮。相反，我们学习从一小组3D模型和更大一组未对准的真实图像的照片 - 真实的延迟渲染，两者都很容易在实践中获得。同时，我们获得了真实图像的准确内在分解，同时不需要配对的地面真相。我们的实验证实，渲染和分解的联合治疗确实有益，我们的方法优于定性和定量的最先进的图像到图像转换基调。

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