HDNET: Exploiting HD Maps for 3D Object Detection
This work addresses the problem of improving 3D object detection for autonomous driving systems by incorporating HD map information, offering a significant performance boost for a critical safety component.
This paper introduces a single-stage 3D object detector, HDNET, that leverages High-Definition (HD) maps to enhance detection performance and robustness. It extracts geometric and semantic features from HD maps and includes a map prediction module for scenarios where maps are unavailable, achieving state-of-the-art performance on KITTI and a large-scale benchmark while running at 20 FPS.
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.