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arXiv · 2108.10843

Bridging Unsupervised and Supervised Depth from Focus via All-in-Focus Supervision

Ning-Hsu Wang·Ren Wang·Yu-Lun Liu·Yu-Hao Huang·Yu-Lin Chang·Chia-Ping Chen·Kevin Jou
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Abstract

Depth estimation is a long-lasting yet important task in computer vision. Most of the previous works try to estimate depth from input images and assume images are all-in-focus (AiF), which is less common in real-world applications. On the other hand, a few works take defocus blur into account and consider it as another cue for depth estimation. In this paper, we propose a method to estimate not only a depth map but an AiF image from a set of images with different focus positions (known as a focal stack). We design a shared architecture to exploit the relationship between depth and AiF estimation. As a result, the proposed method can be trained either supervisedly with ground truth depth, or \emph{unsupervisedly} with AiF images as supervisory signals. We show in various experiments that our method outperforms the state-of-the-art methods both quantitatively and qualitatively, and also has higher efficiency in inference time.

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Ning-Hsu Wang, Ren Wang, Yu-Lun Liu, Yu-Hao Huang, Yu-Lin Chang, Chia-Ping Chen, Kevin Jou. 2021-08-24. Bridging Unsupervised and Supervised Depth from Focus via All-in-Focus Supervision. https://arxiv.org/abs/2108.10843

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