Unsupervised Learning Based Focal Stack Camera Depth Estimation
We propose an unsupervised deep learning based method to estimate depth from focal stack camera images. On the NYU-v2 dataset, our method achieves much better depth estimation accuracy compared to single-image based methods.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep LearningDepth EstimationSimilar Papers 제목 키워드 기반
Dynamic Fusion Network For Light Field Depth Estimation
Focus based methods have shown promising results for the task of depth estimation. However, most existing focus based depth estimation approaches depend on maximal sharpness of the focal stack. Out of focus information i…
Depth EstimationDeep Depth from Focal Stack with Defocus Model for Camera-Setting Invariance
We propose a learning-based depth from focus/defocus (DFF), which takes a focal stack as input for estimating scene depth. Defocus blur is a useful cue for depth estimation. However, the size of the blur depends on not o…
Depth EstimationDense Depth from Event Focal Stack
We propose a method for dense depth estimation from an event stream generated when sweeping the focal plane of the driving lens attached to an event camera. In this method, a depth map is inferred from an ``event focal s…
Depth EstimationDeep Depth from Focus with Differential Focus Volume
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 f…
Unsupervised Simultaneous Depth-from-defocus and Depth-from-focus
If the accuracy of depth estimation from a single RGB image could be improved it would be possible to eliminate the need for expensive and bulky depth sensing hardware. The majority of efforts toward this end have been f…
Depth Estimation