Keypoint-based Static Object Removal from Photographs

When taking photos of historical buildings and landmarks, one can often encounter more modern and less attractive objects blocking the view (poles, electricity cables, road signs, and others). Photographers strive to take the unobstructed shot, without random objects popping up in the image. However, they avoid deleting unwanted objects with inpainting or exemplar-based methods, because it can introduce notable artefacts. We propose a new algorithm for removing large static objects such as road signs, sitting people, and parked cars from a photograph keeping its originality and unique details. We do not use artificial patterns to fill covered regions, only two shots taken from different angles. Usually, removing long thin objects such as road sign poles cause image deformations, but our approach avoids this. For comparison, we created a dataset from "Caltech Buildings" and "Kaggle Architecture" Datasets and added road signs, cars, and other objects to photos. We compared our approach with state-of-the-art methods such as Deep Image Prior, Gated Convolution, and the Region Filling by block sampling. The real photographs of historical building demonstrate the effectiveness of our algorithm. Code and example images are available on GitHub(1).