  3D perception using sensors under vehicle industrial standard is the rigid
demand in autonomous driving. MEMS LiDAR emerges with irresistible trend due to
its lower cost, more robust, and meeting the mass-production standards.
However, it suffers small field of view (FoV), slowing down the step of its
population. In this paper, we propose LEAD, i.e., LiDAR Extender for Autonomous
Driving, to extend the MEMS LiDAR by coupled image w.r.t both FoV and range. We
propose a multi-stage propagation strategy based on depth distributions and
uncertainty map, which shows effective propagation ability. Moreover, our depth
outpainting/propagation network follows a teacher-student training fashion,
which transfers depth estimation ability to depth completion network without
any scale error passed. To validate the LiDAR extension quality, we utilize a
high-precise laser scanner to generate a ground-truth dataset. Quantitative and
qualitative evaluations show that our scheme outperforms SOTAs with a large
margin. We believe the proposed LEAD along with the dataset would benefit the
community w.r.t depth researches.
