While a traditional camera only captures one point of view of a scene, a
plenoptic or light-field camera, is able to capture spatial and angular
information in a single snapshot, enabling depth estimation from a single
acquisition. In this paper, we present a new metric depth estimation algorithm
using only raw images from a multi-focus plenoptic camera. The proposed
approach is especially suited for the multi-focus configuration where several
micro-lenses with different focal lengths are used. The main goal of our blur
aware depth estimation (BLADE) approach is to improve disparity estimation for
defocus stereo images by integrating both correspondence and defocus cues. We
thus leverage blur information where it was previously considered a drawback.
We explicitly derive an inverse projection model including the defocus blur
providing depth estimates up to a scale factor. A method to calibrate the
inverse model is then proposed. We thus take into account depth scaling to
achieve precise and accurate metric depth estimates. Our results show that
introducing defocus cues improves the depth estimation. We demonstrate the
effectiveness of our framework and depth scaling calibration on relative depth
estimation setups and on real-world 3D complex scenes with ground truth
acquired with a 3D lidar scanner.

本文提出了一种新的使用多焦点光场相机的原始图像进行度量深度估计的算法，该算法特别适用于使用多个具有不同焦距的微透镜的多焦点配置。我们通过集成对应关系和模糊度线索利用模糊可知信息来改进失焦立体图像的视差估计，从而改进视差估计，并提出了逆投影模型的方法以达到精确和准确的度量深度估计。最终的结果表明，引入模糊度线索可以改善深度估计。我们通过对相对深度估计和通过 3D 激光雷达扫描仪获得的真实世界 3D 复杂场景进行地面真实度评估来证明我们的算法和深度尺度校准的有效性。