https://scholars.lib.ntu.edu.tw/handle/123456789/607309
標題: | S3Net: A single stream structure for depth guided image relighting | 作者: | Yang H.-H Chen W.-T Kuo S.-Y. SY-YEN KUO |
關鍵字: | Computer vision;Deep learning;Image enhancement;Depthmap;Encoder-decoder;Guided images;Illumination settings;Original images;Relighting;Stream structure;Structure network;Decoding | 公開日期: | 2021 | 起(迄)頁: | 276-283 | 來源出版物: | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops | 摘要: | 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 3rd highest SSIM in the NTIRE 2021 Depth Guided Any-to-any Relighting Challenge. ? 2021 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115989342&doi=10.1109%2fCVPRW53098.2021.00037&partnerID=40&md5=d18e07a061bd0d3db66d754a5f6a683a https://scholars.lib.ntu.edu.tw/handle/123456789/607309 |
ISSN: | 21607508 | DOI: | 10.1109/CVPRW53098.2021.00037 |
顯示於: | 電機工程學系 |
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