MapFusion: A General Framework for 3D Object Detection with HDMaps
This addresses the perception challenge for autonomous vehicles by leveraging underutilized map data, though it is incremental as it builds on existing detectors.
The paper tackles the problem of 3D object detection in autonomous driving by integrating High Definition Maps into detection pipelines, achieving improvements of 1.27 to 2.79 points in mean Average Precision across three baselines.
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.