  In this paper we show that High-Definition (HD) maps provide strong priors
that can boost the performance and robustness of modern 3D object detectors.
Towards this goal, we design a single stage detector that extracts geometric
and semantic features from the HD maps. As maps might not be available
everywhere, we also propose a map prediction module that estimates the map on
the fly from raw LiDAR data. We conduct extensive experiments on KITTI as well
as a large-scale 3D detection benchmark containing 1 million frames, and show
that the proposed map-aware detector consistently outperforms the
state-of-the-art in both mapped and un-mapped scenarios. Importantly the whole
framework runs at 20 frames per second.
