单光子探测器具有高灵敏度和快速响应的特性，但在目标特性未知的情况下，无法直接确定每个像素点所需要的采样积分时间。因此，由于目标表面结构特性和反射率差异，探测器的距离估计值会出现采样不足或是采样饱和的现象。提出一种基于光子计数的自适应快速深度成像方法。该方法利用噪声光子和信号光子的飞行时间的不同特性，改进传统的基于最大似然估计算法的成像模型，自适应决定每个像素点的采样积分时间，并估计其最佳深度信息。实验结果表明，即使在低信噪比条件下，相比于固定采样积分时间成像方法，此算法仍能够更快、更准确重构出目标的深度图像。

Conventionally, for a direct- detection 3D imaging light detection and ranging (LIDAR) system, it′s impossible to directly and accurately determine the pixel-wise signal-acquisition time in the case that the scene properties are unknown. Thus, a time-of-flight depth sensor produces noisy range data due to different scene properties such as surface materials and reflectivity, frequently including either a saturated or severely noisy depth estimated. A photon-counting adaptive depth imaging strategy for 3D imaging LIDAR is presented. By improving the traditional imaging model based on the maximum likelihood estimation algorithm, an appropriate signalacquisition time is adaptively selected, and then determine an optimal depth for each pixel. Experiment demonstrates that the proposed algorithm can be more quick and more accurate to reconstruct the scene depth images even in the low light-level environment.