Rephotography is the process of recapturing the photograph of a location from the same perspective in which it was captured earlier. A rephotographed image is the best presentation to visualize and study the social changes of a location over time. Traditionally, only expert artists and photographers are capable of generating the rephotograph of any specific location. Manual editing or human eye judgment that is considered for generating rephotographs normally requires a lot of precision, effort and is not always accurate. In the era of computer science and deep learning, computer vision techniques make it easier and faster to perform precise operations to an image. Until now many research methodologies have been proposed for rephotography but none of them is fully automatic. Some of these techniques require manual input by the user or need multiple images of the same location with 3D point cloud data while others are only suggestions to the user to perform rephotography. In historical records/archives most of the time we can find only one 2D image of a certain location. Computational rephotography is a challenge in the case of using only one image of a location captured at different timestamps because it is difficult to find the accurate perspective of a single 2D historical image. Moreover, in the case of building rephotography, it is required to maintain the alignments and regular shape. The features of a building may change over time and in most of the cases, it is not possible to use a features detection algorithm to detect the key features. In this research paper, we propose a methodology to rephotograph house images by combining deep learning and traditional computer vision techniques. The purpose of this research is to rephotograph an image of the past based on a single image. This research will be helpful not only for computer scientists but also for history and cultural heritage research scholars to study the social changes of a location during a specific time period, and it will allow users to go back in time to see how a specific place looked in the past. We have achieved good, fully automatic rephotographed results based on façade segmentation using only a single image.

中文翻译：

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使用深度学习对房屋图像进行单图像立面分割和计算重拍

重拍是从之前拍摄的相同角度重新拍摄某个位置的照片的过程。重新拍摄的图像是可视化和研究一个位置随时间推移的社会变化的最佳演示。传统上，只有专业的艺术家和摄影师才能生成任何特定位置的重照。考虑用于生成重拍的手动编辑或人眼判断通常需要大量的精确度和努力，并且并不总是准确的。在计算机科学和深度学习时代，计算机视觉技术使对图像执行精确操作变得更加容易和快捷。到目前为止，已经提出了许多用于重拍的研究方法，但没有一种是全自动的。其中一些技术需要用户手动输入，或者需要具有 3D 点云数据的同一位置的多个图像，而其他技术只是建议用户执行重新拍摄。大多数情况下，在历史记录/档案中，我们只能找到某个位置的一张 2D 图像。在仅使用在不同时间戳捕获的位置的一张图像的情况下，计算重拍是一项挑战，因为很难找到单个 2D 历史图像的准确透视图。此外，在建筑物重照的情况下，需要保持对齐和规则的形状。建筑物的特征可能会随着时间而变化，并且在大多数情况下，不可能使用特征检测算法来检测关键特征。在这篇研究论文中，我们提出了一种通过结合深度学习和传统计算机视觉技术来重新拍摄房屋图像的方法。这项研究的目的是根据单个图像重新拍摄过去的图像。这项研究不仅有助于计算机科学家，也有助于历史和文化遗产研究学者研究一个地点在特定时间段内的社会变化，它可以让用户及时回到过去，看看特定地点的样子在过去。基于仅使用单个图像的外观分割，我们获得了良好的全自动重拍结果。这项研究不仅有助于计算机科学家，也有助于历史和文化遗产研究学者研究一个地点在特定时间段内的社会变化，它可以让用户及时回到过去，看看特定地点的样子在过去。基于仅使用单个图像的外观分割，我们获得了良好的全自动重拍结果。这项研究不仅有助于计算机科学家，也有助于历史和文化遗产研究学者研究一个地点在特定时间段内的社会变化，它可以让用户及时回到过去，看看特定地点的样子在过去。基于仅使用单个图像的外观分割，我们获得了良好的全自动重拍结果。