  3D object detection is a key perception component in autonomous driving. Most
recent approaches are based on Lidar sensors only or fused with cameras. Maps
(e.g., High Definition Maps), a basic infrastructure for intelligent vehicles,
however, have not been well exploited for boosting object detection tasks. In
this paper, we propose a simple but effective framework - MapFusion to
integrate the map information into modern 3D object detector pipelines. In
particular, we design a FeatureAgg module for HD Map feature extraction and
fusion, and a MapSeg module as an auxiliary segmentation head for the detection
backbone. Our proposed MapFusion is detector independent and can be easily
integrated into different detectors. The experimental results of three
different baselines on large public autonomous driving dataset demonstrate the
superiority of the proposed framework. By fusing the map information, we can
achieve 1.27 to 2.79 points improvements for mean Average Precision (mAP) on
three strong 3d object detection baselines.
