Continuous depth maps are reconstructed based on depth estimation from light-field data of axially distributed image sensing (ADS). In the proposed method, the light field of ADS is introduced, and the light-field trajectory function is presented, which formulates the relationship between image point positions in different axially captured images and the depth of object point. The depth value of an object is achieved by searching the light-field trajectory through statistical regression, which leads to an accurate depth estimation by making use of the structure information among the densely sampled views in light-field data. Moreover, as regions with smooth texture and occlusions do not contain reliable depth information, an energy minimization-based depth map optimization is carried out from initial estimations to obtain continuous and robust depth maps. Experimental results demonstrate that the reconstructed depth maps are accurate, continuous, and able to preserve more details. Moreover, the synthesized light-field images calculated by reconstructed depth map show good effect in 3D light-field display, and the validity of the proposed method is verified.

中文翻译：

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基于轴向分布光场的3D光场显示深度重建

基于深度估计，从轴向分布图像传感（ADS）的光场数据中重构出连续的深度图。该方法引入了ADS的光场，并给出了光场轨迹函数，公式化了不同轴向捕获图像中像点位置与物点深度之间的关系。通过统计回归搜索光场轨迹来获得对象的深度值，这可以通过利用光场数据中密集采样的视图之间的结构信息来进行准确的深度估计。此外，由于具有平滑纹理和遮挡的区域不包含可靠的深度信息，从初始估计中进行基于能量最小化的深度图优化，以获得连续且鲁棒的深度图。实验结果表明，重建后的深度图准确，连续且能够保留更多细节。此外，通过重建深度图计算得到的合成光场图像在3D光场显示中显示出良好的效果，并验证了所提方法的有效性。