Artificial intelligence including deep learning and 3D reconstruction methods is changing the daily life of people. Now, an unmanned aerial vehicle that can move freely in the air and avoid harsh ground conditions has been commonly adopted as a suitable tool for 3D reconstruction. The traditional 3D reconstruction mission based on drones usually consists of two steps: image collection and offline post-processing. But there are two problems: one is the uncertainty of whether all parts of the target object are covered, and another is the tedious post-processing time. Inspired by modern deep learning methods, we build a telexistence drone system with an onboard deep learning computation module and a wireless data transmission module that perform incremental real-time dense reconstruction of urban cities by itself. Two technical contributions are proposed to solve the preceding issues. First, based on the popular depth fusion surface reconstruction framework, we combine it with a visual-inertial odometry estimator that integrates the inertial measurement unit and allows for robust camera tracking as well as high-accuracy online 3D scan. Second, the capability of real-time 3D reconstruction enables a new rendering technique that can visualize the reconstructed geometry of the target as navigation guidance in the HMD. Therefore, it turns the traditional path-planning-based modeling process into an interactive one, leading to a higher level of scan completeness. The experiments in the simulation system and our real prototype demonstrate an improved quality of the 3D model using our artificial intelligence leveraged drone system.