Donné Simon, Goossens Bart, Philips Wilfried
IPI-UGent-imec, B-9000 Ghent, Belgium.
Sensors (Basel). 2017 Aug 23;17(9):1939. doi: 10.3390/s17091939.
Stereomatching is an effective way of acquiring dense depth information from a scene when active measurements are not possible. So-called lightfield methods take a snapshot from many camera locations along a defined trajectory (usually uniformly linear or on a regular grid-we will assume a linear trajectory) and use this information to compute accurate depth estimates. However, they require the locations for each of the snapshots to be known: the disparity of an object between images is related to both the distance of the camera to the object and the distance between the camera positions for both images. Existing solutions use sparse feature matching for camera location estimation. In this paper, we propose a novel method that uses dense correspondences to do the same, leveraging an existing depth estimation framework to also yield the camera locations along the line. We illustrate the effectiveness of the proposed technique for camera location estimation both visually for the rectification of epipolar plane images and quantitatively with its effect on the resulting depth estimation. Our proposed approach yields a valid alternative for sparse techniques, while still being executed in a reasonable time on a graphics card due to its highly parallelizable nature.
当无法进行主动测量时，立体匹配是从场景中获取密集深度信息的有效方法。所谓的光场方法是沿着定义的轨迹（通常是均匀线性或在规则网格上——我们将假设为线性轨迹）从多个相机位置拍摄快照，并利用这些信息来计算准确的深度估计。然而，它们要求知道每个快照的位置：图像之间物体的视差与相机到物体的距离以及两张图像相机位置之间的距离都有关系。现有的解决方案使用稀疏特征匹配来估计相机位置。在本文中，我们提出了一种新颖的方法，该方法利用密集对应关系来做同样的事情，借助现有的深度估计框架来确定沿直线的相机位置。我们通过对极平面图像校正的可视化以及它对所得深度估计的影响的定量分析，说明了所提出技术在相机位置估计方面的有效性。我们提出的方法为稀疏技术提供了一种有效的替代方案，同时由于其高度可并行化的特性，仍能在图形卡上以合理的时间执行。
