This paper presents experimental methods and results for 3D environment reconstruction from monocular video augmented with inertial data. One application targets sparsely furnished room interiors, using high quality handheld video with a normal field of view, and linear accelerations and angular velocities from an attached inertial measurement unit. A second application targets natural terrain with manmade structures, using heavily compressed aerial video with a narrow field of view, and position and orientation data from the aircraft navigation system. In both applications, the translational and rotational offsets between the camera and inertial reference frames are initially unknown, and only a
small fraction of the scene is visible in any one video frame. We start by estimating sparse structure and motion from 2D feature tracks using a Kalman filter and/or repeated, partial bundle adjustments requiring bounded time per video frame. The first application additionally incorporates a weak assumption of bounding perpendicular planes to minimize a tendency of the motion estimation to drift, while the second application requires tight integration of the navigational data to alleviate the poor conditioning caused by the narrow field of view. This is followed by dense structure recovery via graph-cut-based multi-view stereo, meshing, and optional mesh simplification. Finally, input images are texture-mapped onto the 3D surface for rendering. We show sample results from multiple, novel viewpoints.