Deep Depth from Focus with Differential Focus Volume

• URL: http://arxiv.org/abs/2112.01712v1
• Date: Fri, 3 Dec 2021 04:49:51 GMT
• Title: Deep Depth from Focus with Differential Focus Volume
• Authors: Fengting Yang, Xiaolei Huang, Zihan Zhou
• Abstract summary: We propose a convolutional neural network (CNN) to find the best-focused pixels in a focal stack and infer depth from the focus estimation. The key innovation of the network is the novel deep differential focus volume (DFV)
• Score: 17.505649653615123
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Depth-from-focus (DFF) is a technique that infers depth using the focus change of a camera. In this work, we propose a convolutional neural network (CNN) to find the best-focused pixels in a focal stack and infer depth from the focus estimation. The key innovation of the network is the novel deep differential focus volume (DFV). By computing the first-order derivative with the stacked features over different focal distances, DFV is able to capture both the focus and context information for focus analysis. Besides, we also introduce a probability regression mechanism for focus estimation to handle sparsely sampled focal stacks and provide uncertainty estimation to the final prediction. Comprehensive experiments demonstrate that the proposed model achieves state-of-the-art performance on multiple datasets with good generalizability and fast speed.

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