论文标题
SAINET：立体声意识到具有生成网络后面的对象后面的涂料
SaiNet: Stereo aware inpainting behind objects with generative networks
论文作者
论文摘要
在这项工作中，我们提出了一个端到端网络，用于立体声一致的图像插图，目的是为对象背后的大型缺失区域内置。所提出的模型由使用部分卷积的边缘引导的Unet样网络组成。我们通过引入差异损失来实施多视图立体声一致性。更重要的是，我们开发了一种训练方案，其中从代表对象闭合的现实立体声掩码中学到了模型，而不是更常见的随机掩模。该技术以有监督的方式进行了培训。与以前的最新技术相比，我们的评估显示了竞争成果。
In this work, we present an end-to-end network for stereo-consistent image inpainting with the objective of inpainting large missing regions behind objects. The proposed model consists of an edge-guided UNet-like network using Partial Convolutions. We enforce multi-view stereo consistency by introducing a disparity loss. More importantly, we develop a training scheme where the model is learned from realistic stereo masks representing object occlusions, instead of the more common random masks. The technique is trained in a supervised way. Our evaluation shows competitive results compared to previous state-of-the-art techniques.
