【论文阅读】【三维目标检测】RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving

文章目录

• RT3D
• • 体素化
  • 检测网络
  • 实验结果

RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving
IEEE ROBOTICS AND AUTOMATION LETTERS, 2018

RT3D

本方法是个两阶段模型，总体来说就是使用R-FCN在栅格上进行检测。本文时间较早，所以方法上感觉比较简单。

体素化

0.05x0.05的体素，统计高度最大值，最小值和均值。不包括点的用（0,0,0）补充。
The point cloud is projected onto a ground-plane grid with resolution 0.05×0.05 m, where each grid cell records (min(z),ave(z),max(z)) of relevant projected points. Grid cells with no point clouds are assigned a triple of (0, 0, 0).
KITTI的场景一般是70mx80m，这样看，体素化之后的大小是1400x1600的大小。

检测网络

基于ResNet-50的R-FCN。作者将R-FCN在提取ROI之前的卷积，K2C个通道的feature map解释为通过车辆不同位置预测车辆的位置、轮廓。另外作者提到要注意的两点是
Two issues caused by the sparsity of the point cloud need to be addressed: 1) during sliding window on feature maps, there are many anchors which have no data. To reduce computation, we delete these empty anchors; 2) many region proposals contain no vehicles or many region proposals contain simple examples, so online hard example mining (OHEM) [19] is adopted to automatically select hard examples to make training more effective and efficient.

实验结果

精度不高，但是快。Ablation Study也是在讨论一些超参数的设计问题。