For decreasing the effect of background noise, the traditional imaging method of laser radar requires take long time in accumulation sampling and generating statistical histogram of photon countingto obtain the depth estimation of target. A 3D time-domain denoising algorithm based on photon-counting laser radar was proposed in this paper. Combined with the Poisson statistical model, the method proposed did not need to generate photon counting statistic histogram but used the different distribution feature of signal and noise in the time-domain, which increased the detection probability of signal photons and separated the signal from the noise to recover an accurate depth image of scene in the environment of low signal-to-noise rate. Experimental results demonstrate that the method increases the imaging accuracy by 3-fold at least comparing to the traditional maximum likelihood depth estimation. The method is conducive to the use of laser radar 3D imaging in high background noise environment and could broaden the application range of Lidar.