Abstract

Until now, the task of  stitching multiple overlapping images to a bigger, panoramic picture is solely approached with "classical", hardcoded algorithms while deep learning is at most used for speci c subtasks. This talk introduces a novel end-to-end neural network approach to image stitching called StitchNet, which uses a (pretrained) autoencoder and deep convolutional networks. Additionally to presenting several new datasets for the task of supervised image stitching with each 120'000 training and 5'000 validation samples, this talk also presents various experiments with different kinds of existing networks designed for image superresolution and image segmentation adapted to the task of image stitching.