  We introduce visual hints expansion for guiding stereo matching to improve
generalization. Our work is motivated by the robustness of Visual Inertial
Odometry (VIO) in computer vision and robotics, where a sparse and unevenly
distributed set of feature points characterizes a scene. To improve stereo
matching, we propose to elevate 2D hints to 3D points. These sparse and
unevenly distributed 3D visual hints are expanded using a 3D random geometric
graph, which enhances the learning and inference process. We evaluate our
proposal on multiple widely adopted benchmarks and show improved performance
without access to additional sensors other than the image sequence. To
highlight practical applicability and symbiosis with visual odometry, we
demonstrate how our methods run on embedded hardware.
