  Depth guided any-to-any image relighting aims to generate a relit image from
the original image and corresponding depth maps to match the illumination
setting of the given guided image and its depth map. To the best of our
knowledge, this task is a new challenge that has not been addressed in the
previous literature. To address this issue, we propose a deep learning-based
neural Single Stream Structure network called S3Net for depth guided image
relighting. This network is an encoder-decoder model. We concatenate all images
and corresponding depth maps as the input and feed them into the model. The
decoder part contains the attention module and the enhanced module to focus on
the relighting-related regions in the guided images. Experiments performed on
challenging benchmark show that the proposed model achieves the 3 rd highest
SSIM in the NTIRE 2021 Depth Guided Any-to-any Relighting Challenge.
