TY - JOUR AU - Tsai, Yu-Ju AU - Liu, Yu-Lun AU - Ouhyoung, Ming AU - Chuang, Yung-Yu PY - 2020/04/03 Y2 - 2024/03/29 TI - Attention-Based View Selection Networks for Light-Field Disparity Estimation JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 34 IS - 07 SE - AAAI Technical Track: Vision DO - 10.1609/aaai.v34i07.6888 UR - https://ojs.aaai.org/index.php/AAAI/article/view/6888 SP - 12095-12103 AB - <p>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.</p> ER -