论文标题
LIDAR 3D对象检测的点密度感知体素
Point Density-Aware Voxels for LiDAR 3D Object Detection
论文作者
论文摘要
激光雷达已成为自动驾驶中主要的3D对象检测传感器之一。但是，LiDAR随距离增加的分化点模式导致不均匀的采样点云不适合离散化的体积特征提取。当前方法要么依赖于体素化点云，要么使用效率低下的点采样来减轻密度变化引起的有害效应，但在很大程度上忽略了点密度作为特征及其与LIDAR传感器距离的可预测关系。我们提出的解决方案是点密度感知体素网络（PDV），是一种端到端的两阶段激光镜3D对象检测体系结构，旨在解释这些点密度变化。 PDV通过体素质心有效地从3D稀疏卷积主链中定位体素特征。然后，使用内核密度估计（KDE）和自我注意力通过点密度位置编码，通过密度感知的ROI网格池池通过密度感知的ROI网格合并模块聚集了空间局部的体素特征。最后，我们利用激光雷达的点密度到距离关系，以完善我们的最终边界信心。 PDV优于Waymo打开数据集中的所有最新方法，并在Kitti数据集上实现竞争结果。我们为PDV提供代码发布，可在https://github.com/trailab/pdv上找到。
LiDAR has become one of the primary 3D object detection sensors in autonomous driving. However, LiDAR's diverging point pattern with increasing distance results in a non-uniform sampled point cloud ill-suited to discretized volumetric feature extraction. Current methods either rely on voxelized point clouds or use inefficient farthest point sampling to mitigate detrimental effects caused by density variation but largely ignore point density as a feature and its predictable relationship with distance from the LiDAR sensor. Our proposed solution, Point Density-Aware Voxel network (PDV), is an end-to-end two stage LiDAR 3D object detection architecture that is designed to account for these point density variations. PDV efficiently localizes voxel features from the 3D sparse convolution backbone through voxel point centroids. The spatially localized voxel features are then aggregated through a density-aware RoI grid pooling module using kernel density estimation (KDE) and self-attention with point density positional encoding. Finally, we exploit LiDAR's point density to distance relationship to refine our final bounding box confidences. PDV outperforms all state-of-the-art methods on the Waymo Open Dataset and achieves competitive results on the KITTI dataset. We provide a code release for PDV which is available at https://github.com/TRAILab/PDV.
