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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The ACM Symposium on User Interface Software and Technology (UIST) is the premier forum for innovations in human-computer interfaces. This year, we curated more than 40 demonstrations, aimed to allow conference attendees to look, touch, and witness new and inspiring technologies live. Here, we selected four projects that showcase the very best in the community. Chris Harrison and Nicolai Marquardt, UIST 2015 Demo Committee Chairs

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