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.

翻译：传统相机只能捕捉场景的一个视角，而全光相机（或光场相机）能在单次拍摄中获取空间与角度信息，从而实现单次采集下的深度估计。本文提出一种新颖的度量深度估计算法，仅利用多焦点全光相机的原始图像。该方法尤其适用于采用多种不同焦距微透镜的多焦点配置。我们的模糊感知深度估计算法（BLADE）的核心目标是通过整合对应度线索与散焦线索，提升离焦立体图像的视差估计质量。因此，我们充分利用了以往被视为缺点的模糊信息。我们明确推导了包含散焦模糊的逆投影模型，可提供至尺度因子的深度估计值。随后提出一种标定该逆模型的方法，通过考虑深度缩放因子实现精确的度量深度估计。实验结果表明，引入散焦线索能有效改善深度估计性能。我们在相对深度估计场景以及通过3D激光雷达扫描仪获取真实数据的真实复杂三维场景中，验证了所提框架与深度缩放标定方法的有效性。