On 3D imaging, light field cameras typically are of single shot, and however, they heavily suffer from low spatial resolution and depth accuracy. In this paper, by employing an optical projector to project a group of single high-frequency phase-shifted sinusoid patterns, we propose a phase guided light field algorithm to significantly improve both the spatial and depth resolutions for off-the-shelf light field cameras. First, for correcting the axial aberrations caused by the main lens of our light field camera, we propose a deformed cone model to calibrate our structured light field system. Second, over wrapped phases computed from patterned images, we propose a stereo matching algorithm, i.e. phase guided sum of absolute difference, to robustly obtain the correspondence for each pair of neighbored two lenslets. Finally, by introducing a virtual camera according to the basic geometrical optics of light field imaging, we propose a reorganization strategy to reconstruct 3D point clouds with spatial-depth high resolution. Experimental results show that, compared with the state-of-the-art active light field methods, the proposed reconstructs 3D point clouds with a spatial resolution of 1280$\times$720 with factors 10$\times$ increased, while maintaining the same high depth resolution and needing merely a single group of high-frequency patterns.

翻译：在三维成像领域，光场相机通常具有单次拍摄的优点，但严重受限于低空间分辨率和深度精度。本文通过使用光学投影仪投射一组单一高频相移正弦条纹图案，提出了一种相位引导光场算法，显著提升现有光场相机的空间与深度分辨率。首先，为校正光场相机主透镜引起的轴向像差，我们提出了一种变形锥体模型来标定所构建的结构光场系统。其次，基于条纹图像计算得到的包裹相位，我们提出了一种立体匹配算法——相位引导绝对差值和法，以稳健地获取相邻两个微透镜单元之间的对应关系。最后，依据光场成像的基本几何光学原理引入虚拟相机，提出一种重组策略，重建具有空间-深度高分辨率的三维点云。实验结果表明，与现有最先进的主动光场方法相比，本方法重建的三维点云空间分辨率可达1280×720像素（提升10倍），同时保持相同的高深度分辨率，且仅需一组高频条纹图案。