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.