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

AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020 Yu-Ju TsaiYu-Lun LiuMing OuhyoungYung-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... (read more)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK BENCHMARK
Depth Estimation 4D Light Field Dataset LFattNet BadPix(0.07) 3.756 # 1
MSE 1.904 # 1
BadPix(0.03) 6.823 # 1
BadPix(0.01) 17.226 # 1

Methods used in the Paper


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