Dense depth map is essential in areas such as autonomous driving and robotics, but today’s depth sensors can only produce sparse depth measurements. Therefore, it is necessary to complete it. In all auxiliary modalities, RGB images are commonly used and easily obtained. Many current methods use RGB and sparse depth information in depth completion. However, most of them simply use channel concatenation or element-wise addition to fuse the information of the two modalities, without considering the confidence of each modalities in different scenarios. This study proposes a dynamic gated fusion module, which is guided by the sparse distribution of input sparse depth and information of both RGB and sparse depth feature, thus fusing two modal features more efficiently by generating dynamic weights. And designed an efficient feature extraction structure according to the data characteristics of different modalities. Comprehensive experiments show the effectiveness of each model. And the network proposed in this paper uses lightweight model to achieve advanced results on two challenging public data sets KITTI depth completion and NYU depth v2. Which shows our method has a good balance of performance and speed.