  We present a near real-time solution for 3D reconstruction from aerial images
captured by consumer UAVs. Our core idea is to simplify the multi-view stereo
problem into a series of two-view stereo matching problems. Our method applies
to UAVs equipped with only one camera and does not require special stereo
capturing setups. We found that the neighboring two video frames taken by UAVs
flying at a mid-to-high cruising altitude can be approximated as left and right
views from a virtual stereo camera. Leveraging GPU-accelerated real-time stereo
estimation and efficient PnP correspondence solving algorithms, our system
simultaneously predicts scene geometry and camera position/orientation from the
virtual stereo cameras. Also, this method allows user-selection of varying
baseline lengths, which provides more flexibility given the trade-off between
camera resolution, effective measuring distance, flight altitude and mapping
accuracy. Our method outputs dense point clouds at a constant speed of 25
frames per second and is validated on a variety of real-world datasets with
satisfactory results.
