Single photon lidar is widely used to obtain depth and intensity information of 3D scenes. In a complex scene, there are multiple targets with different depths and different reflectivity. In the case of few return photons and high background noise, traditional methods cannot make targeted treatment for these targets. As a result, a single photon lidar depth estimation technique for complex scenes is proposed. The method makes full use of the time-domain correlation of the echo signal to conduct global multi-depth windowing on the lidar 3D point cloud data in the time domain. Additionally, the weighted filling of vacant pixels uses spatial correlation. Under the optimization framework, a Poisson distribution model is established based on the pre-processed lidar 3D point cloud data. To acquire an accurate depth measurement, the minimum of the cost function is finally found using the alternating direction multiplier approach. Experimental results demonstrate that the proposed method enhances the reconstruction signal-to-noise ratio of the estimated depth image by at least 15% compared with other methods. Compared with other methods under complicated sceneries from a distance, it successfully raises the estimated quality of depth images and increases the robustness to a low photon level.