Papers › Attention-based View Selection Networks for Light-field Disparity Estimation

Attention-based View Selection Networks for Light-field Disparity Estimation

7 Feb 2020AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020 2archive 2025-07-28

Yu-Ju Tsai, Yu-Lun Liu, Ming Ouhyoung, Yung-Yu Chuang

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.

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