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.

翻译：深度引导任何到任何图像的点亮, 目的是从原始图像和相应的深度地图中生成一幅图像, 以匹配给定的引导图像的光照设置及其深度地图。 根据我们所知, 这项任务是以前文献中尚未解决的新挑战。 为了解决这个问题, 我们建议建立一个深层次学习的神经单一流结构网络, 名为 S3Net, 以深度引导图像点亮。 这个网络是一个编码器- 解码器模型。 我们将所有图像和相应的深度地图作为输入输入并输入模型。 解码器部分包含关注模块和强化模块, 以聚焦于受引导图像中与亮光有关的区域。 在具有挑战性的基准上进行的实验显示, 拟议的模型实现了NTIRE 2021 深度引导任何光化挑战中的第3个最高 SISIM 。