  We describe a method to infer dense depth from camera motion and sparse depth
as estimated using a visual-inertial odometry system. Unlike other scenarios
using point clouds from lidar or structured light sensors, we have few hundreds
to few thousand points, insufficient to inform the topology of the scene. Our
method first constructs a piecewise planar scaffolding of the scene, and then
uses it to infer dense depth using the image along with the sparse points. We
use a predictive cross-modal criterion, akin to `self-supervision,' measuring
photometric consistency across time, forward-backward pose consistency, and
geometric compatibility with the sparse point cloud. We also launch the first
visual-inertial + depth dataset, which we hope will foster additional
exploration into combining the complementary strengths of visual and inertial
sensors. To compare our method to prior work, we adopt the unsupervised KITTI
depth completion benchmark, and show state-of-the-art performance on it. Code
available at:
https://github.com/alexklwong/unsupervised-depth-completion-visual-inertial-odometry.
