Deep Depth from Focus with Differential Focus Volume

Autor:	Yang, Fengting, Huang, Xiaolei, Zhou, Zihan
Rok vydání:	2021
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: 17 pages; CVPR2022 accepted
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2112.01712 Zobrazit plný text záznamu View this record from Arxiv