When Geiger mode avalanche photo diode （Gm-APD） lidar is used to image targets obscured by dense smoke， the strong backscattering and absorption of laser light by the smoke severely limit the ability of traditional algorithms in extracting the target signal hidden in the smoke signal. To this end， we propose a Gm-APD lidar imaging algorithm based on dual-parameter estimation for imaging in smoke environments. First， this paper introduces a trigger model based on Gm-APD lidar and describes the principle for solving the actual received echo signal based on the detection probability. In addition， based on the collision theory of photons and smoke particles， as well as the Mie scattering theory， the physical relationship between the two parameters of the gamma model is derived. Second， a dual-parameter estimation algorithm is proposed based on the derived relationship， which considers approaches to accurately estimate μ and k. Finally， simulation and indoor experiments are conducted. The correctness of the relationship between μ and k is verified based on these simulation experiments， and the imaging ability of the proposed algorithm in the presence of smoke is verified through indoor experiments. The experimental results reveal that compared with traditional algorithms， the target recovery of the image reconstructed by the proposed algorithm increases by 73%， and the structural similarity increases by 0.228 9. Thus， this study effectively improves the target perception ability of Gm-APD lidar in smoke environments.