https://scholars.lib.ntu.edu.tw/handle/123456789/581487
標題: | Attention-based view selection networks for light-field disparity estimation | 作者: | Tsai Y.-J Liu Y.-L Ouhyoung M Chuang Y.-Y. YUNG-YU CHUANG |
關鍵字: | Benchmarking; Attention-based views; Depth Estimation; Depth Map; Disparity estimations; Light fields; State-of-the-art performance; View selection; Artificial intelligence | 公開日期: | 2020 | 起(迄)頁: | 12095-12103 | 來源出版物: | AAAI 2020 - 34th AAAI Conference on Artificial Intelligence | 摘要: | This paper introduces a novel deep network for estimating depth maps from a light field image. For utilizing the views more effectively and reducing redundancy within views, we propose a view selection module that generates an attention map indicating the importance of each view and its potential for contributing to accurate depth estimation. By exploring the symmetric property of light field views, we enforce symmetry in the attention map and further improve accuracy. With the attention map, our architecture utilizes all views more effectively and efficiently. Experiments show that the proposed method achieves state-of-the-art performance in terms of accuracy and ranks the first on a popular benchmark for disparity estimation for light field images. Copyright 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096034956&partnerID=40&md5=48135c83edd36e742f7d86641b40301e https://scholars.lib.ntu.edu.tw/handle/123456789/581487 |
顯示於: | 資訊工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。