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Learning Multi-modal Information for Robust Light Field Depth Estimation

2021-04-13 06:51:27
Yongri Piao, Xinxin Ji, Miao Zhang, Yukun Zhang

Abstract

Light field data has been demonstrated to facilitate the depth estimation task. Most learning-based methods estimate the depth infor-mation from EPI or sub-aperture images, while less methods pay attention to the focal stack. Existing learning-based depth estimation methods from the focal stack lead to suboptimal performance because of the defocus blur. In this paper, we propose a multi-modal learning method for robust light field depth estimation. We first excavate the internal spatial correlation by designing a context reasoning unit which separately extracts comprehensive contextual information from the focal stack and RGB images. Then we integrate the contextual information by exploiting a attention-guide cross-modal fusion module. Extensive experiments demonstrate that our method achieves superior performance than existing representative methods on two light field datasets. Moreover, visual results on a mobile phone dataset show that our method can be widely used in daily life.

Abstract (translated)

URL

https://arxiv.org/abs/2104.05971

PDF

https://arxiv.org/pdf/2104.05971.pdf


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