A new Kinect depth map pretreatment algorithm was presented for the lower accuracy problem compared with the original depth information in the field of three-Dimensional (3D) scene measurement for robot's perception. Firstly, a measuring and sampling model of the depth map was developed to realize the Monte Carlo uncertainty evaluation model. Secondly, the depth value intervals were calculated to judge and filter the noise pixels. Finally, noise points were repaired with mean-value of the estimation intervals. The experimental results show that the algorithm can effectively suppress and repair the noise pixels while keeping the depth gradient and values of non-noise pixels. The Mean Square Error (MSE) of depth map after preprocessing is reduced by 15.25% to 28.79%, and the object profiles remain unchanged compared with the JBF (Joint Bilateral Filtering) based on color and depth map. Therefore, it achieves the purpose of improving the depth information accuracy in 3D scenes.