Deep Depth from Focus with Differential Focus Volume
Deep Depth from Focus with Differential Focus Volume
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DOI：
10.1109/cvpr52688.2022.01231
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Fengting Yang;Xiaolei Huang;Zihan Zhou
中科院分区：
文献类型：
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作者：
Fengting Yang;Xiaolei Huang;Zihan Zhou
深度 - 焦点（DFF）是一种使用相机的焦点变化来渗透深度的技术。在这项工作中，我们提出了一个卷积神经网络（CNN），以在焦点堆栈中找到最注重的像素，并从焦点估计中推断深度。网络的关键创新是新型的深度差异量量（DFV）。通过计算具有不同焦距上堆叠特征的一阶导数，DFV能够捕获焦点和上下文信息以进行焦点分析。此外，我们还引入了一种概率回归机制，以进行焦点估计，以处理稀疏采样的焦点堆栈并为最终预测提供不确定性估计。全面的实验表明，所提出的模型可以在多个数据集上具有良好的概括性和快速速度的最先进性能。
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.