Due to the numerous applications employed 3D data such as target detection and recognition, three-dimensional (3D) active imaging draws great interest recently. Employing a pulsed laser as the illumination source and an intensified sensor as the image sensor, the 3D active imaging method emits and then records laser pulses to infer the distance between the target and the sensor. One of the limitations of the 3D active imaging is that acquiring depth map with high depth resolution requires a full range sweep, as well as a large number of detections, which limits the detection speed. In this work, a compressed gating method combining the 3D active imaging and compressive sensing (CS) is proposed on the basis of the random gating method to achieve the depth map reconstruction from a significantly reduced number of detections. Employing random sequences to control the sensor gate, this method estimates the distance and reconstructs the depth map in the framework of CS. A simulation was carried out to estimate the performance of the proposed method. A scene generated by the 3ds Max was employed as target and a reconstruction algorithm was used to recover the depth map in the simulation. The simulation results have shown that the proposed method can reconstruct the depth map with slight reconstruction error using as low as 7% detections that the conventional method requires and achieve perfect reconstruction from about 10% detections under the same depth resolution. It has also indicated that the number of detections required is affected by depth resolution, noise generated by a variety of reasons and complexity of the target scene. According to the simulation results, the compressed gating method is able to be used in the case of long range with high depth resolution and robust to various types of noise. In addition, the method is able to be used for multiple-return signals measurement without increase in the number of detections.