We present a novel end-to-end network, MANet, for light field depth estimation. MANet is a parameter-effective and effi-cient multi-scale aggregated network, which is about 3 times smaller and 3 times faster than the current top-performing method Epinet. The MANet architecture is performed for estimating depth from light field plenoptic cameras, and experimental results show that the proposed MANet outperforms state-of-the-art methods on HCI, CVIA-HCI and EPFL Lytro light field datasets.
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