Fast Depth Estimation for Light Field Cameras
Fast Depth Estimation for Light Field Cameras
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DOI：
10.1109/tip.2020.2970814
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发表时间：
2020-01-01
影响因子：
10.6
通讯作者：
Mishiba, Kazu
中科院分区：
文献类型：
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作者：
Mishiba, Kazu
光场图像的快速深度估计对于诸如基于图像的渲染和重聚焦的多个应用是重要的任务。大多数先前的光场深度估计方法涉及高计算成本。因此，在本研究中，我们提出了一种快速的深度估计方法的基础上，多视角立体匹配的光场图像。类似于其他传统的方法，我们的方法包括初始深度估计和细化。对于初始估计，我们为每个像素使用一位特征，并通过使用快速算法对所有视点组合进行求和来计算匹配成本。为了减少计算时间，我们引入了离线视点选择策略和成本体积插值。我们的精化过程解决了目标函数由l（1）数据和平滑项组成的最小化问题。虽然这个问题可以通过图切割算法来解决，但它在计算上是昂贵的，因此，我们提出了一个基于快速加权中值滤波器的近似求解器。对合成数据和真实数据的实验表明，该方法在所有方法中以最短的计算时间达到了具有竞争力的精度。
Fast depth estimation for light field images is an important task for multiple applications such as image-based rendering and refocusing. Most previous approaches to light field depth estimation involve high computational costs. Therefore, in this study, we propose a fast depth estimation method based on multi-view stereo matching for light field images. Similar to other conventional methods, our method consists of initial depth estimation and refinement. For the initial estimation, we use a one-bit feature for each pixel and calculate matching costs by summing all combinations of viewpoints with a fast algorithm. To reduce computational time, we introduce an offline viewpoint selection strategy and cost volume interpolation. Our refinement process solves the minimization problem in which the objective function consists of l(1) data and smoothness terms. Although this problem can be solved via a graph cuts algorithm, it is computationally expensive; therefore, we propose an approximate solver based on a fast-weighted median filter. Experiments on synthetic and real-world data show that our method achieves competitive accuracy with the shortest computational time of all methods.