Structured-light and stereo cameras, which are widely used to construct point clouds for robotic applications, have different limitations on estimating depth values. Structured-light cameras fail in black, transparent, and reflective objects, which influence the light path; stereo cameras fail in texture-less objects. In this work, we propose a depth fusion model that complements these two types of methods to generate high-quality point clouds for short-range robotic applications. The model first determines the fusion weights from the two input depth images and then refines the fused depth using color features. We construct a dataset containing the aforementioned challenging objects and report the performance of our proposed model. The results reveal that our method reduces the average L1 distance on depth prediction by 75% and 52% compared with the original depth output of the structured-light camera and the stereo model, respectively. A noticeable improvement on the Iterative Closest Point (ICP) algorithm can be achieved by using the refined depth images output from our method.