As interest in image-based rendering increases, the need for multiview inpainting is emerging. Despite of rapid progresses in single-image inpainting based on deep learning approaches, they have no constraint in obtaining color consistency over multiple inpainted images. We target object removal in large-scale indoor spaces and propose a novel pipeline of multiview inpainting to achieve color consistency and boundary consistency in multiple images. The first step of the pipeline is to create color prior information on masks by coloring point clouds from multiple images and projecting the colored point clouds onto the image planes. Next, a generative inpainting network accepts a masked image, a color prior image, imperfect guideline, and two different masks as inputs and yields the refined guideline and inpainted image as outputs. The color prior and guideline input ensure color and boundary consistencies across multiple images. We validate our pipeline on real indoor data sets quantitatively using consistency distance and similarity distance, metrics we defined for comparing results of multiview inpainting and qualitatively.

随着对基于图像的渲染的兴趣增加，对多视图修复的需求正在出现。尽管基于深度学习方法的单幅图像修复取得了快速进展，但它们在获得多幅修复图像上的颜色一致性方面没有任何限制。我们以大型室内空间中的对象去除为目标，并提出了一种新颖的多视图修复管道，以实现多幅图像的颜色一致性和边界一致性。流水线的第一步是通过对多个图像中的点云进行着色并将着色的点云投影到图像平面上，从而在蒙版上创建颜色先验信息。接下来，生成式修复网络接受蒙版图像，彩色先验图像，不完善的准则以及两个不同的蒙版作为输入，并生成精炼的准则和修复后的图像作为输出。颜色先验和准则输入可确保跨多个图像的颜色和边界一致性。我们使用一致距离和相似距离（我们定义用于比较多视图修复结果和定性比较的指标）对基于真实室内数据集的管道进行定量验证。