论文标题
从光场快速准确的基于光流的深度图估计
Fast and Accurate Optical Flow based Depth Map Estimation from Light Fields
论文作者
论文摘要
深度图估计是计算机视觉中的至关重要的任务，并且最近出现了新的方法，因为这种新成像方式与基于立体镜面图像或多视图的常见方法相比，这种新成像方式捕获了有关光线角度方向的更多信息。在本文中，我们根据现有的光流估计方法提出了一种新的深度估计方法。光流估计器应用于沿着光场的角度尺寸拍摄的一系列图像，该图像产生了几个差异图估计值。考虑到准确性和效率，我们选择特征流方法作为光流估计器。由于其时空边缘感知过滤属性，我们获得的不同差异图估计值非常一致，这允许快速，简单的聚合步骤创建一个单个差异图，然后可以将其转换为深度图。由于差异图估计值是一致的，因此我们还可以从每个差异估计中创建一个深度图，然后在3D空间中汇总不同的深度图以创建一个单个密集的深度图。
Depth map estimation is a crucial task in computer vision, and new approaches have recently emerged taking advantage of light fields, as this new imaging modality captures much more information about the angular direction of light rays compared to common approaches based on stereoscopic images or multi-view. In this paper, we propose a novel depth estimation method from light fields based on existing optical flow estimation methods. The optical flow estimator is applied on a sequence of images taken along an angular dimension of the light field, which produces several disparity map estimates. Considering both accuracy and efficiency, we choose the feature flow method as our optical flow estimator. Thanks to its spatio-temporal edge-aware filtering properties, the different disparity map estimates that we obtain are very consistent, which allows a fast and simple aggregation step to create a single disparity map, which can then converted into a depth map. Since the disparity map estimates are consistent, we can also create a depth map from each disparity estimate, and then aggregate the different depth maps in the 3D space to create a single dense depth map.
