论文标题
HDNET：用于3D对象检测的高清图
HDNET: Exploiting HD Maps for 3D Object Detection
论文作者
论文摘要
在本文中，我们表明高清图（HD）图提供了强大的先验，可以提高现代3D对象探测器的性能和稳健性。为了实现这一目标，我们设计了一个单阶段检测器，该检测器从高清图中提取几何和语义特征。由于地图可能无法到处可用，因此我们还提出了一个地图预测模块，该模块可以根据RAW LIDAR数据即时估算地图。我们对Kitti进行了广泛的实验，以及包含100万帧的大规模3D检测基准，并表明所提出的地图感知探测器在映射和未映射方案中始终优于最先进的探测器。重要的是，整个框架以每秒20帧的速度运行。
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.
